diff --git a/.dockerignore b/.dockerignore
index 50127a91d..29404d838 100644
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,143 +1,23 @@
-# Project
-results
-output
-docs
-
-# Git
-.git
-
-# Pantsbuild
-.pants.d
-.pants.workdir.file_lock
-.pants.workdir.file_lock.lock_message
-
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
+**/__pycache__
+*,cover
+*.log
+*.pyc
+*.pyd
+*.pyo
 .Python
-build/
-develop-eggs/
-#dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-pip-wheel-metadata/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
+.cache
 .coverage
 .coverage.*
-.cache
-nosetests.xml
+.git
+.github
+.tox
+Dockerfile
 coverage.xml
-*.cover
-.hypothesis/
-.pytest_cache/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-db.sqlite3
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# IPython
-profile_default/
-ipython_config.py
-
-# pyenv
-.python-version
-
-# pipenv
-#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
-#   However, in case of collaboration, if having platform-specific dependencies or dependencies
-#   having no cross-platform support, pipenv may install dependencies that don’t work, or not
-#   install all needed dependencies.
-#Pipfile.lock
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# Environments
-.env
-.venv
-env/
+docs
+env
+nosetests.xml
+pip-delete-this-directory.txt
+pip-log.txt
+scripts
+tests
 venv/
-ENV/
-env.bak/
-venv.bak/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
-.dmypy.json
-dmypy.json
-
-# Pyre type checker
-.pyre/
-
-# 
-.DS_Store
-
-# Editors
-.idea
-.vscode
diff --git a/.editorconfig b/.editorconfig
deleted file mode 100644
index 7441c6c09..000000000
--- a/.editorconfig
+++ /dev/null
@@ -1,10 +0,0 @@
-root = true
-
-[*]
-end_of_line = lf
-insert_final_newline = true
-charset = utf-8
-
-[*.py]
-indent_style = space
-indent_size = 4
diff --git a/.github/workflows/development-e2e-tests.yml b/.github/workflows/development-e2e-tests.yml
new file mode 100644
index 000000000..2387bb5bd
--- /dev/null
+++ b/.github/workflows/development-e2e-tests.yml
@@ -0,0 +1,13 @@
+name: Comment-based E2E test
+
+on:
+  pull_request_review_comment:
+    types: [created]
+
+jobs:
+  e2e:
+    runs-on: ubuntu-18.04
+    steps:
+      - name: logging
+        if: github.event.comment.body == '/run e2e'
+        run: echo "$GITHUB_REF"
diff --git a/.github/workflows/development.yml b/.github/workflows/development.yml
new file mode 100644
index 000000000..1f0f4301c
--- /dev/null
+++ b/.github/workflows/development.yml
@@ -0,0 +1,57 @@
+name: Lint and test (development)
+
+on:
+  pull_request:
+    branches:
+      - development
+jobs:
+  development:
+    runs-on: ubuntu-18.04
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v2
+
+      - name: Setup Python 3.6
+        uses: actions/setup-python@v1
+        with:
+          python-version: 3.6
+
+      - name: Cache pip packages
+        uses: actions/cache@v1
+        with:
+          path: ~/.cache/pip
+          key: pip-dev-${{ hashFiles('setup.py') }}
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install --upgrade setuptools
+          pip install -e .[dev]
+
+      - name: black
+        run: |
+          black --line-length 100 --check setup.py xain_fl tests
+
+      - name: isort
+        run: |
+          isort --check-only --indent=4 -rc setup.py xain_fl tests
+
+      - name: pylint
+        run: |
+          pylint --rcfile=.pylintrc xain_fl tests
+
+      - name: mypy
+        continue-on-error: true
+        run: |
+          mypy xain_fl tests
+
+      - name: Sphinx check
+        env:
+          SPHINXOPTS: "-W"
+        run: |
+          cd docs/
+          make docs
+
+      - name: PyTest
+        run: |
+          pytest -v
diff --git a/.github/workflows/main.yml b/.github/workflows/master.yml
similarity index 69%
rename from .github/workflows/main.yml
rename to .github/workflows/master.yml
index 1a7ba752c..bd29df39e 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/master.yml
@@ -1,4 +1,4 @@
-name: Lint and test
+name: Lint and test (master)
 
 on:
   pull_request:
@@ -20,7 +20,7 @@ jobs:
         uses: actions/cache@v1
         with:
           path: ~/.cache/pip
-          key: pip-${{ hashFiles('setup.py') }}
+          key: pip-master-${{ hashFiles('setup.py') }}
 
       - name: Install dependencies
         run: |
@@ -30,22 +30,20 @@ jobs:
 
       - name: black
         run: |
-          black --check --exclude "xain_fl/cproto/.*_pb2.*" \
-          setup.py conftest.py benchmarks examples xain_fl
+          black --line-length 100 --check setup.py xain_fl tests
 
       - name: isort
         run: |
-          isort --check-only --indent=4 -rc setup.py conftest.py benchmarks \
-          examples xain_fl
+          isort --check-only --indent=4 -rc setup.py xain_fl tests
 
       - name: pylint
         run: |
-          pylint --rcfile=pylint.ini benchmarks examples xain_fl
+          pylint --rcfile=.pylintrc xain_fl tests
 
       - name: mypy
         continue-on-error: true
         run: |
-          mypy benchmarks examples/* xain_fl
+          mypy xain_fl tests
 
       - name: Sphinx check
         env:
diff --git a/.gitignore b/.gitignore
index bf81b6e6b..0fefa5da5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,13 +1,3 @@
-# Project specific files and directories
-config.cfg
-output/*
-results
-
-# Pantsbuild
-.pants.d
-.pants.workdir.file_lock
-.pants.workdir.file_lock.lock_message
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/.isort.cfg b/.isort.cfg
index a6252817c..0baf8d4be 100644
--- a/.isort.cfg
+++ b/.isort.cfg
@@ -1,7 +1,8 @@
 [settings]
-multi_line_output=3
-include_trailing_comma=True
 force_grid_wrap=0
+force_sort_within_sections=True
+include_trailing_comma=True
+known_third_party=xain_proto,xain_sdk
+line_length=100
+multi_line_output=3
 use_parentheses=True
-line_length=88
-skip_glob=*_pb2*
diff --git a/.pylintrc b/.pylintrc
new file mode 100644
index 000000000..12b7c1a60
--- /dev/null
+++ b/.pylintrc
@@ -0,0 +1,19 @@
+[MESSAGES CONTROL]
+
+disable=bad-continuation,redefined-builtin,duplicate-code,fixme
+
+[TYPECHECK]
+
+# List of members which are set dynamically and missed by pylint inference
+# system, and so shouldn't trigger E1101 when accessed. Python regular
+# expressions are accepted.
+# See: https://github.com/pytorch/pytorch/issues/701#issuecomment-378048755
+generated-members=numpy.*,torch.*
+
+# ignore auto generated grpc files
+ignore-patterns=
+
+[MISCELLANEOUS]
+
+# List of note tags to take in consideration, separated by a comma.
+notes=TODO,FIXME
diff --git a/.readthedocs.yml b/.readthedocs.yml
index 32f3de7a3..3ad81aab8 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -14,4 +14,4 @@ python:
       - method: pip
         path: .
         extra_requirements:
-           - docs
\ No newline at end of file
+           - docs
diff --git a/CHANGELOG.md b/CHANGELOG.md
index aa62a7d6d..e707c83a9 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -20,6 +20,66 @@ For reference, the possible headings are:
 
 ## [Unreleased]
 
+## [0.3.0] - 2020-01-21
+
+- XP-505 cleanup docstrings in xain_fl.coordinator (#228)
+- XP-498 more generic shebangs (#229)
+- XP-510 allow for zero epochs on cli (#227)
+- XP-508 Replace circleci badge (#225)
+- XP-505 docstrings cleanup (#224)
+- XP-333 Replace numproto with xain-proto (#220)
+- XP-499 Remove conftest, exclude tests folder (#223)
+- XP-480 revise message names (#222)
+- XP-436 Reinstate FINISHED heartbeat from Coordinator (#219)
+- XP-308 store aggregated weights in S3 buckets (#215)
+- XP-308 store aggregated weights in S3 buckets (#215)
+- XP-422 ai metrics (#216)
+- XP-119 Fix gRPC testing setup so that it can run on macOS (#217)
+- XP-433 Fix docker headings (#218)
+- Xp 373 add sdk as dependency in fl (#214)
+- DO-49  Create initial buckets (#213)
+- XP-424 Remove unused packages (#212)
+- XP-271 fix pylint issues (#210)
+- XP-374 Clean up docs (#211)
+- DO-43  docker compose minio (#208)
+- XP-384 remove unused files (#209)
+- XP-357 make controller parametrisable (#201)
+- XP 273 scripts cleanup (#206)
+- XP-385 Fix docs badge (#204)
+- XP-354 Remove proto files (#200)
+- DO-17  Add Dockerfiles, dockerignore and docs (#202)
+- XP-241 remove legacy participant and sdk dir (#199)
+- XP-168 update setup.py (#191)
+- XP-261 move tests to own dir (#197)
+- XP-257 cleanup cproto dir (#198)
+- XP-265 move benchmarks to separate repo (#193)
+- XP-255 update codeowners and authors in setup (#195)
+- XP-255 update codeowners and authors in setup (#195)
+- XP-229 Update Readme.md (#189)
+- XP-337 Clean up docs before generation (#188)
+- XP-264 put coordinator as own package (#183)
+- XP-272 Archive rust code (#186)
+- Xp 238 add participant selection (#179)
+- XP-229 Update readme (#185)
+- XP-334 Add make docs into docs make file (#184)
+- XP-291 harmonize docs styles (#181)
+- XP-300 Update docs makefile (#180)
+- XP-228 Update readme (#178)
+- XP-248 use structlog (#173)
+- XP-207 model framework agnostic (#166)
+- XAIN-284 rename package name (#176)
+- XP-251 Add ability to pass params per cmd args to coordinator (#174)
+- XP-167 Add gitter badge (#171)
+- Hotfix badge versions and style (#170)
+- Integrate docs with readthedocs (#169)
+- add pull request template (#168)
+
+## [0.2.0] - 2019-12-02
+
+### Changed
+
+- Renamed package from xain to xain-fl
+
 ## [0.1.0] - 2019-09-25
 
 The first public release of **XAIN**
@@ -32,9 +92,3 @@ The first public release of **XAIN**
 
 [unreleased]: https://github.com/xainag/xain-fl/pulls?utf8=%E2%9C%93&q=merged%3A%3E2019-09-25+
 [0.1.0]: https://github.com/xainag/xain-fl/pulls?utf8=%E2%9C%93&q=merged%3A%3C%3D2019-09-25+
-
-## [0.2.0] - 2019-12-02
-
-### Changed
-
-- Renamed package from xain to xain-fl
diff --git a/CODEOWNERS b/CODEOWNERS
deleted file mode 100644
index 0fe679937..000000000
--- a/CODEOWNERS
+++ /dev/null
@@ -1,25 +0,0 @@
-# See for details: https://help.github.com/en/articles/about-code-owners
-
-# Default code owners
-* @danieljanes @tanertopal @r-marques
-
-## CI config
-/.circleci/ @r-marques @tanertopal
-
-## Benchmarks
-/benchmarks/benchmark/ @danieljanes @tanertopal
-/benchmarks/generator/ @tanertopal
-/benchmarks/ops/ @tanertopal
-
-## Protobufs
-/protobuf/ @r-marques
-
-## Scripts
-/scripts/ @tanertopal @danieljanes
-
-## Python packages
-/xain_fl/datasets/ @tanertopal
-/xain_fl/fl/ @danieljanes
-/xain_fl/grpc/ @r-marques @finiteprods
-/xain_fl/helpers/ @danieljanes @tanertopal
-/xain_fl/types/ @danieljanes @tanertopal
diff --git a/Dockerfile b/Dockerfile
index 3cbf49473..55ebc6283 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,28 +1,29 @@
-FROM python:3.6.8-slim
+FROM python:3.6-alpine
 
-WORKDIR /opt/app
+ENV USER="xain"
+ENV HOST="0.0.0.0"
+ENV PORT="50051"
+ENV PATH="/home/${USER}/.local/bin:${PATH}"
 
-# Create output directory as its expected
-RUN mkdir output
+RUN addgroup -S ${USER} && adduser -S ${USER} -G ${USER}
+RUN apk update && apk add python3-dev build-base git
 
-# Upgrade pip and setuptools
-RUN python -m pip install -U pip==19.3.1 setuptools==41.6.0
+WORKDIR /app
 
-# First copy scripts and setup.py to install dependencies
-# and avoid reinstalling dependencies when only changing the code
-COPY setup.py setup.py
+COPY setup.py .
+COPY xain_fl xain_fl/
+COPY README.md .
 
-# These files are needed for the setup.py to work
-COPY xain_fl/__version__.py xain_fl/__version__.py
-COPY README.md README.md
+RUN pip install -v .
 
-# Install only install_requires
-RUN python setup.py egg_info && \
-    LN=$(awk '/tensorflow/{ print NR; exit }' xain_fl.egg-info/requires.txt) && \
-    IR=$(head -n $LN xain_fl.egg-info/requires.txt | awk '{gsub(/\[.+\]/,"");}1') && \
-    python -m pip install $IR
+# Remove everything, including dot files
+RUN rm -rf ..?* .[!.]* *
 
-COPY xain_fl xain_fl
-COPY protobuf protobuf
+# Drop down to a non-root user
+USER ${USER}
 
-RUN python -m pip install .
+COPY --chown=${USER}:${USER} test_array.npy test_array.npy
+COPY --chown=${USER}:${USER} docker/entrypoint.sh entrypoint.sh
+RUN chmod +x entrypoint.sh
+
+ENTRYPOINT ["./entrypoint.sh"]
diff --git a/Dockerfile.dev b/Dockerfile.dev
new file mode 100644
index 000000000..4ea14a35c
--- /dev/null
+++ b/Dockerfile.dev
@@ -0,0 +1,12 @@
+FROM python:3.6-alpine
+
+RUN apk update && apk add python3-dev build-base git
+
+WORKDIR /app
+COPY setup.py .
+COPY xain_fl xain_fl/
+COPY README.md .
+
+RUN pip install -v -e .
+
+CMD ["python3", "setup.py", "--fullname"]
diff --git a/README.md b/README.md
index fa281d20f..70a9e5009 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,14 @@
-[![CircleCI](https://img.shields.io/circleci/build/github/xainag/xain-fl/master?style=flat-square)](https://circleci.com/gh/xainag/xain-fl/tree/master)
-[![PyPI](https://img.shields.io/pypi/v/xain-fl?style=flat-square)](https://pypi.org/project/xain-fl/)
-[![GitHub license](https://img.shields.io/github/license/xainag/xain-fl?style=flat-square)](https://github.com/xainag/xain-fl/blob/master/LICENSE)
-[![Documentation Status](https://readthedocs.org/projects/xain/badge/?version=latest&style=flat-square)](https://docs.xain.io/en/latest/?badge=latest)
+[![Workflow Lint and test (master)](https://github.com/xainag/xain-fl/workflows/Lint%20and%20test%20%28master%29/badge.svg)](https://github.com/xainag/xain-fl)
+[![PyPI](https://img.shields.io/pypi/v/xain-fl)](https://pypi.org/project/xain-fl/)
+[![GitHub license](https://img.shields.io/github/license/xainag/xain-fl)](https://github.com/xainag/xain-fl/blob/master/LICENSE)
+[![Documentation Status](https://readthedocs.org/projects/xain-fl/badge/?version=latest)](https://xain-fl.readthedocs.io/en/latest/?badge=latest)
 [![Gitter chat](https://badges.gitter.im/xainag.png)](https://gitter.im/xainag)
 
 # XAIN
 
-The XAIN project is building a GDPR-compliance layer for machine learning. The approach relies on Federated Learning
-as enabling technology that allows production AI applications to be fully privacy compliant.
+The XAIN project is building a privacy layer for machine learning so that AI projects can meet compliance such as
+GDPR and CCPA. The approach relies on Federated Learning as enabling technology that allows production AI
+applications to be fully privacy compliant.
 
 Federated Learning also enables different use-cases that are not strictly privacy related such as connecting data
 lakes, reaching higher model performance in unbalanced datasets and utilising AI models on the edge.
@@ -27,21 +28,24 @@ $ python -m pip install xain-fl
 
 ## Install from source
 
-To clone this repository and to install the XAIN-FL project, please execute the following commands:
+Clone this repository:
 
 ```shell
-$ git clone https://github.com/xainag/xain-fl.git
-$ cd xain-fl
-
-$ sh scripts/setup.sh
+git clone https://github.com/xainag/xain-fl.git
 ```
 
-### Verify Installation
+Install this project with the `dev` profile (**NOTE**: it is
+recommended to install the project in a virtual environment):
 
-You can verify the installation by running the tests
+```shell
+cd xain-fl
+pip install -e '.[dev]'
+```
+
+Verify the installation by running the tests
 
 ```shell
-$ pytest
+pytest
 ```
 
 ### Building the Documentation
@@ -76,6 +80,53 @@ For more information about the CLI and its arguments, run:
 $ python xain_fl/cli.py --help
 ```
 
+### Run the Coordinator from a Docker image
+
+There are two docker-compose files, one for development and one for release.
+
+#### Development image
+
+To run the coordinator's development image, first build the Docker image:
+
+```shell
+$ docker build -t xain-fl-dev -f Dockerfile.dev .
+```
+
+Then run the image, mounting the directory as a Docker volume, and call the
+entrypoint:
+
+```shell
+$ docker run -v $(pwd):/app -v '/app/xain_fl.egg-info' xain-fl-dev coordinator
+```
+
+#### Release image
+
+To run the coordinator's release image, first build it:
+
+```shell
+$ docker build -t xain-fl .
+```
+
+And then run it (this example assumes you'll want to use the default port):
+
+```shell
+$ docker run -p 50051:50051 xain-fl
+```
+
+### Docker-compose
+
+#### Development
+
+```shell
+$ docker-compose -f docker-compose-dev.yml up
+```
+
+#### Release
+
+```shell
+$ docker-compose up
+```
+
 ## Related Papers and Articles
 
 - [An introduction to XAIN’s GDPR-compliance Layer for Machine Learning](https://medium.com/xain/an-introduction-to-xains-gdpr-compliance-layer-for-machine-learning-f7c321b31b06)
diff --git a/benchmarks/aggregate.py b/benchmarks/aggregate.py
deleted file mode 100644
index 706dc00cf..000000000
--- a/benchmarks/aggregate.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Used as entry_point for console_script `aggregate` in `setup.py`.
-
-Expects the following flags:
-
-    - group_name
-
-Example:
-    aggregate --group_name=GROUP_NAME
-"""
-from absl import app, flags
-
-from benchmarks.benchmark.aggregation import aggregation
-
-FLAGS = flags.FLAGS
-
-
-def main():
-    flags.mark_flag_as_required("group_name")
-    app.run(main=aggregation.main)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/benchmarks/benchmark/__init__.py b/benchmarks/benchmark/__init__.py
deleted file mode 100644
index b6e3ec107..000000000
--- a/benchmarks/benchmark/__init__.py
+++ /dev/null
@@ -1,13 +0,0 @@
-"""Extensive benchmark suite to evaluate convergence properties of federated learning
-in different settings. The main module in which those scenarious are configured is
-`benchmark.py`.
-"""
-from absl import flags
-
-from .benchmark import benchmarks
-
-flags.DEFINE_string("benchmark_name", None, f"One of: {[k for k in benchmarks]}")
-
-flags.DEFINE_string(
-    "group_name", None, "Group name used to gather the tasks of one benchmark"
-)
diff --git a/benchmarks/benchmark/aggregation/__init__.py b/benchmarks/benchmark/aggregation/__init__.py
deleted file mode 100644
index e81873a94..000000000
--- a/benchmarks/benchmark/aggregation/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Provides various aggregations which can be used per benchmark scenario
-as seen in ~benchmarks.benchmark.benchmark.benchmarks
-"""
diff --git a/benchmarks/benchmark/aggregation/aggregation.py b/benchmarks/benchmark/aggregation/aggregation.py
deleted file mode 100644
index 03443d645..000000000
--- a/benchmarks/benchmark/aggregation/aggregation.py
+++ /dev/null
@@ -1,60 +0,0 @@
-"""Provides compositions of aggregation functions identified by unique names
-to be used in benchmark scenario configuration
-"""
-import os
-from typing import Callable, Dict
-
-from absl import flags
-
-from benchmarks.benchmark.aggregation import (
-    final_task_accuracies,
-    learning_rate,
-    participant_hist,
-    task_accuracies,
-)
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-
-FLAGS = flags.FLAGS
-
-
-logger = get_logger(__name__)
-
-
-def _aggregate():
-    """Calls aggregation defined in a benchmark groups config.json file"""
-    fname = os.path.join(FLAGS.results_dir, FLAGS.group_name, "config.json")
-    config = storage.read_json(fname)
-
-    aggregation_name = config["aggregation_name"]
-
-    aggregations[aggregation_name]()
-
-
-def _flul_aggregation():
-    logger.info("flul_aggregation started")
-    task_accuracies.aggregate()
-    learning_rate.aggregate()
-    participant_hist.participant_history()
-
-
-def _cpp_aggregation():
-    logger.info("cpp_aggregation started")
-    task_accuracies.aggregate()
-    final_task_accuracies.aggregate()
-    participant_hist.participant_history()
-
-
-aggregations: Dict[str, Callable] = {
-    "flul-aggregation": _flul_aggregation,
-    "cpp-aggregation": _cpp_aggregation,
-    "vol-aggregation": _flul_aggregation,
-}
-
-
-def main(_):
-    """Used by ~benchmarks.aggregate.main to create an aggregation of benchmark results
-    identified  by the commandline flag `--group_name`. Has to be invoked through
-    abseil `app.run`.
-    """
-    _aggregate()
diff --git a/benchmarks/benchmark/aggregation/conftest.py b/benchmarks/benchmark/aggregation/conftest.py
deleted file mode 100644
index 125fb0c37..000000000
--- a/benchmarks/benchmark/aggregation/conftest.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import json
-import os
-from pathlib import Path
-
-import pytest
-
-# TODO: Use TaskResults class to read/write task results
-#       to have one central way of reading/writing results
-
-results_unitary = {
-    # Does not contain all keys which are in a actual results.json file
-    # just the ones nessecary for the currently existing tests
-    "task_name": "unitary",
-    "task_label": "unitary label",
-    "partition_id": 0,
-    "hist_opt_configs": None,
-}
-
-
-results_federated = {
-    # Does not contain all keys which are in a actual results.json file
-    # just the ones nessecary for the currently existing tests
-    "task_name": "federated",
-    "task_label": "federated label",
-    "partition_id": None,
-    "hist_opt_configs": [
-        [{"learning_rate": 0.1}, {"learning_rate": 0.1}],
-        [{"learning_rate": 0.2}, {"learning_rate": 0.2}],
-    ],
-}
-
-
-@pytest.fixture
-def group_dir(tmpdir):
-    """Group dir with one unitary and one federated task each containing a results.json"""
-    task_unitary_dir = Path(tmpdir).joinpath("unitary")
-    task_federated_dir = Path(tmpdir).joinpath("federated")
-
-    os.makedirs(task_unitary_dir)
-    os.makedirs(task_federated_dir)
-
-    unitary_results_fname = task_unitary_dir.joinpath("results.json")
-    federated_results_fname = task_federated_dir.joinpath("results.json")
-
-    with open(unitary_results_fname, "w") as outfile:
-        json.dump(results_unitary, outfile, indent=2, sort_keys=True)
-
-    with open(federated_results_fname, "w") as outfile:
-        json.dump(results_federated, outfile, indent=2, sort_keys=True)
-
-    return tmpdir
-
-
-@pytest.fixture
-def unitary_results_json_fname(tmpdir):
-    fname = Path(tmpdir).joinpath("results.json")
-
-    with open(fname, "w") as outfile:
-        json.dump(results_unitary, outfile, indent=2, sort_keys=True)
-
-    return str(fname)
-
-
-@pytest.fixture
-def federated_results_json_fname(tmpdir):
-    fname = Path(tmpdir).joinpath("results.json")
-
-    with open(fname, "w") as outfile:
-        json.dump(results_federated, outfile, indent=2, sort_keys=True)
-
-    return str(fname)
diff --git a/benchmarks/benchmark/aggregation/final_task_accuracies.py b/benchmarks/benchmark/aggregation/final_task_accuracies.py
deleted file mode 100644
index 1e6331e63..000000000
--- a/benchmarks/benchmark/aggregation/final_task_accuracies.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import os
-from typing import Dict, List, Optional, Tuple
-
-from absl import flags
-
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-from xain_fl.types import PlotValues, XticksLabels, XticksLocations
-
-from .plot import plot
-from .results import GroupResult, TaskResult
-
-FLAGS = flags.FLAGS
-
-
-logger = get_logger(__name__)
-
-
-def _read_task_values(task_result: TaskResult) -> Tuple[bool, str, float]:
-    """Reads unitary and federated accuracy from results.json
-
-    Args:
-        fname (str): path to results.json file containing required fields
-
-    Returns
-        class, label, final_accuracy (str, str, float): e.g. ("VisionTask", "cpp01", 0.92)
-    """
-    return (
-        task_result.is_unitary(),
-        task_result.get_label(),
-        task_result.get_final_accuracy(),
-    )
-
-
-def read_all_task_values(group_dir: str) -> List[Tuple[bool, str, float]]:
-    """
-    Reads results directory for given group id and
-    extracts values from results.json files
-
-    Args:
-        group_dir (str): path to directory to be read
-
-    """
-    task_results = GroupResult(group_dir).get_results()
-    # Read accuracies from each file and return list of values in tuples
-    return [_read_task_values(task_result) for task_result in task_results]
-
-
-def group_values_by_class(
-    values: List[Tuple[bool, str, float]]
-) -> Dict[str, List[Tuple[str, float]]]:
-    # Group values by task_class
-    unitary_values = [v for v in values if v[0]]
-    federated_values = [v for v in values if not v[0]]
-
-    grouped_values = {
-        "Unitary": [(label, acc) for _, label, acc in unitary_values],
-        "Federated": [(label, acc) for _, label, acc in federated_values],
-    }
-
-    return grouped_values
-
-
-def _prepare_aggregation_data(
-    group_name: str
-) -> Tuple[List[PlotValues], Optional[Tuple[XticksLocations, XticksLabels]]]:
-    """Constructs and returns curves and xticks_args.
-
-    Args:
-        group_name (str): group name for which to construct the curves
-
-    Returns:
-        A tuple containing a list of `PlotValues` and a list of tuples
-        containing (`XticksLocations`, `XticksLabels`)
-
-    """
-    group_dir = os.path.join(FLAGS.results_dir, group_name)
-    # List of tuples (benchmark_name, unitary_accuracy, federated_accuracy)
-    values = read_all_task_values(group_dir=group_dir)
-    values = sorted(values, key=lambda v: v[1], reverse=True)  # sort by
-
-    assert values, "No values for group found"
-
-    grouped_values = group_values_by_class(values)
-    task_classes = [k for k in grouped_values]
-    indices = list(range(1, len(grouped_values[task_classes[0]]) + 1))
-    labels = [label for label, _ in grouped_values[task_classes[0]]]
-
-    data: List[PlotValues] = []
-
-    for task_class in grouped_values:
-        task_class_values = [acc for _, acc in grouped_values[task_class]]
-        plot_values = (task_class, task_class_values, indices)
-        data.append(plot_values)
-
-    return (data, (indices, labels))
-
-
-def aggregate() -> str:
-    """Plots IID and Non-IID dataset performance comparision
-    Expects FLAGS.group_name to be set
-
-    Returns:
-        str: Absolut path to saved plot
-    """
-    group_name = FLAGS.group_name
-    dname = storage.create_output_subdir(group_name)
-    fname = storage.fname_with_default_dir("plot_final_task_accuracies.png", dname)
-
-    (data, xticks_args) = _prepare_aggregation_data(group_name)
-
-    assert len(data) == 2, "Expecting a list of two curves"
-
-    fpath = plot(
-        data,
-        title="",  # TODO
-        xlabel="IID / Non-IID",
-        ylabel="Accuracy",
-        fname=fname,
-        ylim_max=1.0,
-        xlim_max=12,
-        xticks_args=xticks_args,
-        legend_loc="upper right",
-    )
-
-    logger.info("Data plotted and saved in file", filepath=fpath)
-
-    return fpath
diff --git a/benchmarks/benchmark/aggregation/final_task_accuracies_test.py b/benchmarks/benchmark/aggregation/final_task_accuracies_test.py
deleted file mode 100644
index 723a120cd..000000000
--- a/benchmarks/benchmark/aggregation/final_task_accuracies_test.py
+++ /dev/null
@@ -1,95 +0,0 @@
-import os
-
-import pytest
-from absl import flags
-
-from xain_fl.helpers import sha1
-
-from . import final_task_accuracies
-from .results import TaskResult
-
-FLAGS = flags.FLAGS
-
-
-@pytest.mark.integration
-def test_read_all_task_values(monkeypatch, group_name, results_dir):
-    # Prepare
-    other_group_name = "other_group"
-    assert group_name != other_group_name  # just in case
-
-    group_dir = os.path.join(results_dir, group_name)
-    other_group_dir = os.path.join(results_dir, other_group_name)
-
-    files = [
-        f"{group_dir}/task_1/results.json",
-        f"{group_dir}/task_2/results.json",
-        f"{other_group_dir}/task_1/results.json",
-        f"{other_group_dir}/task_2/results.json",
-    ]
-
-    for fname in files:
-        dname = os.path.dirname(fname)
-        os.makedirs(dname)
-        with open(fname, "x") as f:
-            f.write("{}")
-            f.close()
-
-    def mock_read_task_values(task_result: TaskResult):
-        return task_result
-
-    monkeypatch.setattr(
-        final_task_accuracies, "_read_task_values", mock_read_task_values
-    )
-
-    # Execute
-    actual_results = final_task_accuracies.read_all_task_values(group_dir)
-
-    # Assert
-    assert len(actual_results) == 2
-
-    for r in actual_results:
-        isinstance(r, TaskResult)
-
-
-@pytest.mark.integration
-def test_plot_final_task_accuracies(output_dir, group_name, monkeypatch):
-    # Prepare
-    data = [
-        (
-            "unitary",
-            [0.96, 0.90, 0.81, 0.72, 0.63, 0.54, 0.45, 0.36, 0.27, 0.18, 0.09],
-            range(1, 12, 1),
-        ),
-        (
-            "federated",
-            [0.92, 0.89, 0.87, 0.85, 0.83, 0.81, 0.80, 0.79, 0.78, 0.77, 0.77],
-            range(1, 12, 1),
-        ),
-    ]
-    fname = f"plot_final_task_accuracies.png"
-    expected_filepath = os.path.join(output_dir, group_name, fname)
-    expected_sha1 = "09270962b7be7d8a92961603712b229bd107cdb3"
-
-    xticks_locations = range(1, 12, 1)
-    xticks_labels = [chr(i) for i in range(65, 77, 1)]  # A, B, ..., K
-
-    def mock_prepare_aggregation_data(_: str):
-        return (data, (xticks_locations, xticks_labels))
-
-    monkeypatch.setattr(
-        final_task_accuracies,
-        "_prepare_aggregation_data",
-        mock_prepare_aggregation_data,
-    )
-
-    # Execute
-    actual_filepath = final_task_accuracies.aggregate()
-
-    # If any error occurs we will be able to look at the plot. If the the ploting
-    # logic is changed the file under this path can be used to get the new hash
-    # after evaluating the rendered plot
-    print(actual_filepath)
-
-    # Assert
-    assert expected_filepath == actual_filepath
-    assert expected_sha1 == sha1.checksum(actual_filepath), "Checksum not matching"
diff --git a/benchmarks/benchmark/aggregation/learning_rate.py b/benchmarks/benchmark/aggregation/learning_rate.py
deleted file mode 100644
index 96cdf6ceb..000000000
--- a/benchmarks/benchmark/aggregation/learning_rate.py
+++ /dev/null
@@ -1,115 +0,0 @@
-import os
-from typing import List, Optional, Tuple
-
-from absl import flags
-
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-from xain_fl.types import PlotValues
-
-from .plot import plot
-from .results import GroupResult, TaskResult
-
-FLAGS = flags.FLAGS
-
-
-logger = get_logger(__name__)
-
-
-def _read_task_values(task_result: TaskResult) -> Tuple[str, Optional[List[float]]]:
-    """Reads unitary and federated accuracy from results.json
-
-    Args:
-        fname (str): path to results.json file containing required fields
-
-    Returns
-        class, label, final_accuracy (str, str, float): e.g. ("VisionTask", "cpp01", 0.92)
-    """
-    return (task_result.get_label(), task_result.get_learning_rates())
-
-
-def read_all_task_values(group_dir: str) -> List[Tuple[str, List[float]]]:
-    """
-    Reads results directory for given group_dir and returns a list of
-    tuples with label and list of learning rates
-
-    Args:
-        group_dir (str): path to directory to be read
-
-    """
-    task_results = GroupResult(group_dir).get_results()
-    # Reatur accuracies from each file and return list of values in tuples
-    all_tasks = [_read_task_values(task_result) for task_result in task_results]
-
-    federated_tasks = [
-        (label, learning_rates)
-        for label, learning_rates in all_tasks
-        if learning_rates is not None
-    ]
-
-    return federated_tasks
-
-
-def _prepare_aggregation_data(group_name: str) -> List[PlotValues]:
-    """Constructs and returns learning rate curves
-
-    Args:
-        group_name (str): group name for which to construct the curves
-
-    Returns:
-        A list of `PlotValues`.
-    """
-    group_dir = os.path.join(FLAGS.results_dir, group_name)
-    # List of tuples (benchmark_name, unitary_accuracy, federated_accuracy)
-    labels_and_lrs = read_all_task_values(group_dir=group_dir)
-
-    assert labels_and_lrs, "No values for group found"
-
-    return [
-        (label, lrs, [i for i in range(1, len(lrs) + 1, 1)])
-        for label, lrs in labels_and_lrs
-    ]
-
-
-def aggregate() -> str:
-    """Plots learning rate for federated tasks in a group
-    Expects FLAGS.group_name to be set
-
-    Returns:
-        str: Absolut path to saved plot
-    """
-    group_name = FLAGS.group_name
-    dname = storage.create_output_subdir(group_name)
-    fname = storage.fname_with_default_dir("plot_learning_rates.png", dname)
-
-    data = _prepare_aggregation_data(group_name)
-
-    ylim_max: float = 0
-    xlim_max = 0
-
-    for _, lrs, ylabel in data:
-        if ylabel is not None:
-            xlim_max = max(ylabel + [xlim_max])
-
-        for lr in lrs:
-            ylim_max = max(lr, ylim_max)
-
-    ylim_max *= 1.1
-    xlim_max += 1
-
-    assert data, "Expecting a list with at least one item"
-
-    fpath = plot(
-        data,
-        title="Optimizer learning rates for federated training tasks",
-        xlabel="round",
-        ylabel="learning rate",
-        fname=fname,
-        ylim_max=ylim_max,
-        xlim_max=xlim_max,
-        legend_loc="upper right",
-    )
-
-    logger.info("Data plotted and saved in file", filepath=fpath)
-
-    return fpath
diff --git a/benchmarks/benchmark/aggregation/learning_rate_test.py b/benchmarks/benchmark/aggregation/learning_rate_test.py
deleted file mode 100644
index 617b2988c..000000000
--- a/benchmarks/benchmark/aggregation/learning_rate_test.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-
-import pytest
-from absl import flags
-
-from xain_fl.helpers import sha1
-from xain_fl.logger import get_logger
-
-from . import learning_rate
-
-FLAGS = flags.FLAGS
-
-logger = get_logger(__name__)
-
-
-@pytest.mark.integration
-def test_read_all_task_values(group_dir):
-    # Prepare
-    expected_results = [("federated label", [0.1, 0.2])]
-
-    # Execute
-    actual_results = learning_rate.read_all_task_values(group_dir)
-
-    # Assert
-    assert actual_results == expected_results
-
-
-@pytest.mark.integration
-def test_plot_learning_rate(output_dir, group_name, monkeypatch):
-    # Prepare
-    data = [
-        ("federated 1 - label", [0.10, 0.05, 0.03, 0.02], [1, 2, 3, 4]),
-        ("federated 2 - label", [0.09, 0.07, 0.06, 0.04], [1, 2, 3, 4]),
-    ]
-    fname = f"plot_learning_rates.png"
-    expected_filepath = os.path.join(output_dir, group_name, fname)
-    expected_sha1 = "599daf7563f41289a8eed2b59aba3c5d312eeab1"
-
-    def mock_prepare_aggregation_data(_: str):
-        return data
-
-    monkeypatch.setattr(
-        learning_rate, "_prepare_aggregation_data", mock_prepare_aggregation_data
-    )
-
-    # Execute
-    actual_filepath = learning_rate.aggregate()
-
-    # If any error occurs we will be able to look at the plot. If the the ploting
-    # logic is changed the file under this path can be used to get the new hash
-    # after evaluating the rendered plot
-    logger.info(actual_filepath)
-
-    # Assert
-    assert expected_filepath == actual_filepath
-    assert expected_sha1 == sha1.checksum(actual_filepath), "Checksum not matching"
diff --git a/benchmarks/benchmark/aggregation/participant_hist.py b/benchmarks/benchmark/aggregation/participant_hist.py
deleted file mode 100644
index 09ff1d7e7..000000000
--- a/benchmarks/benchmark/aggregation/participant_hist.py
+++ /dev/null
@@ -1,142 +0,0 @@
-"""Functions for getting participant selection history"""
-
-import os
-from typing import List, Tuple
-
-import numpy as np
-from absl import app, flags
-from numpy import ndarray
-
-from benchmarks.benchmark.aggregation.plot import plot_history_data
-from benchmarks.benchmark.aggregation.results import GroupResult, TaskResult
-from benchmarks.helpers.storage import create_output_subdir, fname_with_default_dir
-from xain_fl.logger import get_logger
-from xain_fl.types import Metrics
-
-FLAGS = flags.FLAGS
-
-logger = get_logger(__name__)
-
-
-def participant_history() -> List[str]:
-    """Plot participant selection histories for group name flag.
-
-    For each task result in the group name flag extract the task metrics (number of
-    participants, task label, hist metrics), transform them into heatmap data as participant
-    indices x training rounds and plot/save them as participant selection history.
-
-    Returns:
-        ~typing.List[str]: File paths for saved plots.
-    """
-
-    group_name: str = FLAGS.group_name
-    dir_name: str = create_output_subdir(dname=group_name)
-    file_pre_name: str = fname_with_default_dir(
-        fname="plot_participant_history_{}.png", dname=dir_name
-    )
-    file_paths: List[str] = list()
-
-    # Getting history metrics data from results.json
-    hist_metrics_group: List[Tuple[int, str, List[List[Metrics]]]] = get_hist_metrics(
-        group_name=group_name
-    )
-
-    # Creates heatmap data for each task metric in group metrics
-    matrices: List[Tuple[str, ndarray]] = list(map(heatmap_data, hist_metrics_group))
-
-    for task_matrix in matrices:
-        label: str = task_matrix[0]
-        matrix: ndarray = task_matrix[1]
-
-        file_path: str = plot_history_data(
-            matrix=matrix,
-            title="Participant Selection History",
-            file_name=file_pre_name.format(label),
-            save=True,
-            show=False,
-        )
-        file_paths.append(file_path)
-
-    logger.info("Task data plotted and saved in file", filepath=file_paths)
-
-    return file_paths
-
-
-def get_hist_metrics(group_name: str) -> List[Tuple[int, str, List[List[Metrics]]]]:
-    """Get the task metrics for a group name.
-
-    Extract number of participants, task label and hist metrics for each federated task
-    metric in a group name and put them into a list.
-
-    Args:
-        group_name (str): Group name for which metrics should be extracted.
-
-    Returns:
-        List[Tuple[int, str, List[List[Metrics]]]]: List of metrics for each task.
-    """
-
-    group_dir: str = os.path.join(FLAGS.results_dir, group_name)
-    task_results: List[TaskResult] = GroupResult(group_dir=group_dir).get_results()
-
-    # Extract metrics for each federated task result in group result
-    metrics: List[Tuple[int, str, List[List[Metrics]]]] = [
-        read_task_metrics(task_result=task_result)
-        for task_result in task_results
-        if task_result.is_unitary() is False
-    ]
-
-    return metrics
-
-
-def heatmap_data(metric: Tuple[int, str, List[List[Metrics]]]) -> Tuple[str, ndarray]:
-    """Heatmap data for a given task metric.
-
-    Creates the heatmap data as participant x train rounds numpy matrix for a given task metric
-    and sets those matrix values to 1, that indices correspond to participant indices and train
-    rounds in the given task history metrics.
-
-    Args:
-        metric (Tuple[int, List[List[Metrics]]]): Task metric consisting of number of participants
-            and hist metrics for a task.
-
-    Returns:
-        ~typing.Tuple[str, numpy.ndarray]: Task label and participant x train rounds matrix with
-            value 1 for each participant indice
-    """
-
-    num_participants, task_label, hist_metrics = metric
-    train_rounds: int = len(hist_metrics)
-
-    # Create heatmap matrix with participants as rows and train rounds as columns
-    heat_map: ndarray = np.zeros((num_participants, train_rounds))
-
-    # Collect participants indices for each training round
-    rows: ndarray = np.asarray(hist_metrics, dtype=object)[:, :, 0].astype(int)
-    # Array of linspace training rounds, each wrapped into an array
-    columns: ndarray = np.split(np.arange(train_rounds), train_rounds)
-    heat_map[rows, columns] = 1
-
-    return (task_label, heat_map)
-
-
-def read_task_metrics(task_result: TaskResult) -> Tuple[int, str, List[List[Metrics]]]:
-    """Get number of participants, task label and history metrics for a task result.
-
-    Args:
-        task_result (TaskResult): Results data for a task.
-
-    Returns:
-        Tuple[int, str, List[List[Metrics]]]: Number of participants, task label and hist
-            metrics in a tuple.
-    """
-
-    return (
-        task_result.get_num_participants(),
-        task_result.get_label(),
-        task_result.get_hist_metrics(),
-    )
-
-
-def app_run_participant_hist():
-    flags.mark_flag_as_required("group_name")
-    app.run(main=lambda _: participant_history())
diff --git a/benchmarks/benchmark/aggregation/plot.py b/benchmarks/benchmark/aggregation/plot.py
deleted file mode 100644
index 1ed80b5ff..000000000
--- a/benchmarks/benchmark/aggregation/plot.py
+++ /dev/null
@@ -1,211 +0,0 @@
-from typing import List, Optional, Tuple
-
-import matplotlib
-import numpy as np
-from absl import flags
-from matplotlib.colors import ListedColormap
-from numpy import ndarray
-
-from benchmarks.helpers import storage
-from xain_fl.types import PlotValues
-
-matplotlib.use("AGG")
-
-# To avoid issues with tkinter we need to set the renderer
-# for matplotlib before importing pyplot
-# As isort would move this line under the "import matplotlib"
-# We need to skip isort explicitly
-# pylint: disable-msg=wrong-import-position, wrong-import-order
-import matplotlib.pyplot as plt  # isort:skip
-
-FORMAT = "png"
-
-FLAGS = flags.FLAGS
-
-
-def plot(
-    data: List[PlotValues],
-    title: Optional[str] = None,
-    xlabel: str = "Epoch",
-    ylabel: str = None,
-    fname: Optional[str] = None,
-    save: bool = True,
-    show: bool = False,
-    ylim_max: float = 1.0,
-    xlim_max: float = 40.0,
-    xticks_args: Optional[Tuple[List[int], List[str]]] = None,
-    legend_loc: str = "lower right",
-    vline: bool = False,
-) -> str:
-    """Wrapper for plt.plot so have uniform plots in the project
-
-    Args:
-        data (List[Tuple[name, values, Optional[indices]]]): List of tuples where each
-            represents a line in the plot with tuple
-            beeing (name, values, Optional[indices])
-        save (bool): Indicates if plot should be saved to disk. Defaults to True
-
-    Returns:
-        str: For save=True returns absolut path to saved file otherwise None
-    """
-    assert fname is not None
-
-    # if fname is an absolute path use fname directly otherwise assume
-    # fname is filename and prepend output_dir
-    fname_abspath = storage.fname_with_default_dir(fname, FLAGS.output_dir)
-
-    plt.figure()
-    plt.ylim(0.0, ylim_max)
-    plt.xlim(0.0, xlim_max)
-
-    if xticks_args is not None:
-        xticks_locations, xticks_labels = xticks_args
-        # if any label has length > 3 rotate labels by 90 degrees
-        rot = 90 if any([len(l) > 3 for l in xticks_labels]) else 0
-        plt.xticks(xticks_locations, xticks_labels, rotation=rot)
-
-    if title is not None:
-        plt.title(title)
-
-    plt.xlabel(xlabel)
-    plt.ylabel(ylabel)
-
-    if vline:
-        plt.axvline(x=50.0)
-
-    data.sort(key=lambda c: c[0])
-
-    legend = []
-    for name, values, indices in data:
-        legend.append(name)
-
-        if indices is None:
-            # x values are optional and default to range(len(values))
-            plt.plot(values)
-        else:
-            assert len(values) == len(indices)
-            plt.plot(indices, values)
-
-    plt.legend(legend, loc=legend_loc)
-
-    # https://matplotlib.org/users/tight_layout_guide.html
-    plt.tight_layout()
-
-    if save:
-        plt.savefig(fname=fname_abspath, format=FORMAT)
-    if show:
-        plt.show()
-    plt.close()
-
-    return fname_abspath
-
-
-def plot_history_data(
-    matrix: ndarray,
-    file_name: str,
-    title: Optional[str] = None,
-    xlabel: str = "Training rounds",
-    ylabel: str = "Participants",
-    save: bool = True,
-    show: bool = False,
-) -> str:
-    """Plot participant selection history.
-
-    Plots or saves the participant selection history of an input matrix
-    as 2D regular raster with training rounds vs participants.
-
-    Args:
-        matrix (~numpy.ndarray): Image data for a 2D regular raster.
-        file_name (str): File name for storable plot.
-        title (~typing.Optional[str]): Title of the plot.
-        xlabel (str): Label for x-axis.
-        ylabel (str): Label for y-axis.
-        save (bool): If the plot should be stored as png.
-        show (bool): If the plot should be shown.
-
-    Returns:
-        str: File name of the plot as absolute path.
-    """
-
-    file_name_abspath: str = storage.fname_with_default_dir(
-        fname=file_name, dname=FLAGS.output_dir
-    )
-
-    # Creating figure with subplot
-    _, ax = plt.subplots(figsize=(11, 9))
-
-    # more information about colormaps: https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html
-    color_map_name: str = "YlGn"
-    color_map: ListedColormap = prepare_colormap(name=color_map_name)
-
-    im = ax.imshow(matrix, cmap=color_map, interpolation="nearest", aspect="auto")
-
-    ax.figure.colorbar(im, ax=ax)
-
-    # maxima for x and y axis
-    x_max: int = matrix.shape[1]
-    y_max: int = matrix.shape[0]
-
-    # Major ticks
-    ax.set_xticks(np.arange(0, x_max, 1))
-    ax.set_yticks(np.arange(0, y_max, 5))
-
-    # Labels for major ticks
-    ax.set_xticklabels(np.arange(1, x_max, 1))
-    ax.set_yticklabels(np.arange(0, y_max, 5))
-
-    # Display only each 2nd x-axis tick label
-    for x_tick_label in ax.xaxis.get_ticklabels()[::2]:
-        x_tick_label.set_visible(False)
-
-    # Minor ticks
-    ax.set_xticks(np.arange(-0.5, x_max, 1), minor=True)
-    ax.set_yticks(np.arange(-0.5, y_max, 1), minor=True)
-
-    # turn off gridline visibility for x and y axis
-    ax.tick_params(axis="both", which="minor", length=0)
-
-    # Gridlines based on minor ticks
-    ax.grid(which="minor", color="w", linestyle="-", linewidth=2)
-
-    ax.set_xlabel(xlabel, fontsize=14)
-    ax.set_ylabel(ylabel, fontsize=14)
-
-    # Set plot title if present
-    if title is not None:
-        ax.set_title(title, fontsize=14)
-
-    plt.tight_layout()
-
-    # Saving and showing plot
-    if save:
-        plt.savefig(fname=file_name_abspath, format=FORMAT)
-    if show:
-        plt.show(block=True)
-    plt.close()
-
-    return file_name_abspath
-
-
-def prepare_colormap(name: str) -> ListedColormap:
-    """Creates a colormap with color grey as 0 value.
-
-    Prepares a downscaled colormap (256 -> 40) from a colormap name and
-    puts in color grey for value 0.
-
-    Args:
-        name (str): Name of the colormap to be scaled.
-
-    Returns:
-        ~matplotlib.colors.ListedColormap: Downscaled color map.
-    """
-
-    # downscaled colormap (256 -> 40)
-    color_map: ListedColormap = matplotlib.cm.get_cmap(name, 40)
-    map_scaled: ndarray = color_map(np.linspace(0, 1, 40))
-
-    grey: ndarray = np.array([0, 0, 0, 0.1])
-    # put in color grey as first entry
-    map_scaled[0, :] = grey
-
-    return ListedColormap(map_scaled)
diff --git a/benchmarks/benchmark/aggregation/results.py b/benchmarks/benchmark/aggregation/results.py
deleted file mode 100644
index 34a0eae82..000000000
--- a/benchmarks/benchmark/aggregation/results.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""Provides the classes TaskResult and GroupResult which wrap
-the results of a benchmark group and provide easy access to the
-results contained in the results.json files in each tasks results
-"""
-import os
-from abc import ABC
-from typing import List, Optional, cast
-
-from benchmarks.helpers import storage
-from xain_fl.types import Metrics
-
-
-class TaskResult(ABC):
-    """Provides predictable access to task results"""
-
-    def __init__(self, fname: str):
-        self.data = storage.read_json(fname)
-
-    def get_name(self) -> str:
-        return self.data["task_name"]
-
-    def get_label(self) -> str:
-        return self.data["task_label"]
-
-    def get_final_accuracy(self) -> float:
-        return self.data["acc"]
-
-    def get_accuracies(self) -> List[float]:
-        return self.data["hist"]["val_acc"]
-
-    def get_learning_rates(self) -> Optional[List[float]]:
-        """Will extract learning rate for federated task results in each round
-        from hist_opt_configs which has a List[List[Dict[str, any]]] type. The
-        top level list contains ROUNDS elements and the second level lists
-        contain number of participants dictionaries (one for each participant).
-        As unitary task results don't have a history of optimizer configs
-        None will be returned.
-        """
-        if self.is_unitary():
-            return None
-
-        hist_opt_configs = self.data["hist_opt_configs"]
-
-        # Extract learning rate only from the first participant as the participants
-        # share the same learning rate in each round
-        learning_rates = [
-            participants_in_round[0]["learning_rate"]
-            for participants_in_round in hist_opt_configs
-        ]
-
-        return learning_rates
-
-    def get_E(self) -> int:
-        return self.data["E"]
-
-    def is_unitary(self) -> bool:
-        return self.data["partition_id"] is not None
-
-    def get_hist_metrics(self) -> List[List[Metrics]]:
-        """Get history metrics from a task result.
-
-        Extracts history metrics for each training round as list of
-        participant indice and VolumeByClass values.
-
-        Returns:
-            ~typing.List[~typing.List[Metrics]]: List of hist metrics for each training round.
-        """
-
-        hist_metrics = [
-            [tuple(metric) for metric in round_metric]
-            for round_metric in self.data["hist_metrics"]
-        ]
-        # mypy is not able to handle list comprehension here correctly
-        return cast(List[List[Metrics]], hist_metrics)
-
-    def get_num_participants(self) -> int:
-        """Get the number of participants.
-
-        Returns:
-            int: Number of participants.
-        """
-
-        return self.data["num_participants"]
-
-
-class GroupResult(ABC):
-    """Provides predictable access to the results of all tasks in a group"""
-
-    def __init__(self, group_dir: str):
-        assert os.path.isdir(group_dir)
-
-        # get list of all directories which contain given substring
-        json_files = [
-            fname
-            for fname in storage.listdir_recursive(group_dir, relpath=False)
-            if fname.endswith("results.json")
-        ]
-
-        if not json_files:
-            raise Exception(f"No values results found in group_dir: {group_dir}")
-
-        self.task_results = [TaskResult(fname) for fname in json_files]
-
-    def get_results(self) -> List[TaskResult]:
-        """Provides a list of TaskResult instances which will enable easy
-        access to the results of a benchmark scenario
-
-        Returns:
-            List[TaskResult]: Each item in the list corrosponds to one task in the
-                benchmark scenario
-        """
-        return self.task_results
diff --git a/benchmarks/benchmark/aggregation/results_test.py b/benchmarks/benchmark/aggregation/results_test.py
deleted file mode 100644
index f77672db0..000000000
--- a/benchmarks/benchmark/aggregation/results_test.py
+++ /dev/null
@@ -1,32 +0,0 @@
-from typing import List
-
-from .results import TaskResult
-
-
-def test_get_learning_rates_unitary(unitary_results_json_fname):
-    # Prepare
-    result = TaskResult(unitary_results_json_fname)
-
-    # Execute
-    learning_rates = result.get_learning_rates()
-
-    # Assert
-    assert learning_rates is None
-
-
-def test_get_learning_rates_federated(federated_results_json_fname):
-    # Prepare
-    result = TaskResult(federated_results_json_fname)
-
-    expected_lr_round_1 = 0.1
-    expected_lr_round_2 = 0.2
-
-    # Execute
-    learning_rates: List[float] = result.get_learning_rates()
-
-    # Assert
-    assert isinstance(learning_rates, list)
-    assert len(learning_rates) == 2
-
-    assert learning_rates[0] == expected_lr_round_1
-    assert learning_rates[1] == expected_lr_round_2
diff --git a/benchmarks/benchmark/aggregation/task_accuracies.py b/benchmarks/benchmark/aggregation/task_accuracies.py
deleted file mode 100644
index 592f8e4db..000000000
--- a/benchmarks/benchmark/aggregation/task_accuracies.py
+++ /dev/null
@@ -1,120 +0,0 @@
-import os
-from typing import List, Tuple
-
-from absl import flags
-
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-from xain_fl.types import PlotValues
-
-from .plot import plot
-from .results import GroupResult, TaskResult
-
-FLAGS = flags.FLAGS
-
-
-logger = get_logger(__name__)
-
-
-def _read_task_values(task_result: TaskResult) -> Tuple[bool, str, List[float], int]:
-    """Reads unitary and federated accuracy from results.json.
-
-    Args:
-        fname (str): Path to results.json file containing required fields.
-
-    Example:
-        >>> print(read_task_values(task_result))
-        (false, "VisionTask", [0.12, 0.33], 5)
-
-    Returns:
-        ~typing.Tuple[bool, str, ~typing.List[float], int]: Tuple consisting of information,
-            if the task is unitary or not, the task label, a list of accuracies and the epochs.
-    """
-    return (
-        task_result.is_unitary(),
-        task_result.get_label(),
-        task_result.get_accuracies(),
-        task_result.get_E(),
-    )
-
-
-def read_all_task_values(group_dir: str) -> List[Tuple[bool, str, List[float], int]]:
-    """Reads results directory for given group id and extracts values from results.json files.
-
-    Args:
-        group_dir (str): Path to group directory to be read.
-
-    Example:
-        >>> print(read_all_task_values(group_dir))
-        [(false, "VisionTask", [0.12, 0.33], 5), (true, "UnitaryTask", [0.23, 0.34], 2),...]
-
-    Returns:
-        ~typing.List[typing.Tuple[bool, str, ~typing.List[float], int]]: List of tuples consisting
-            of information, if the task is unitary or not, the task label, a list of accuracies and
-            the epochs.
-    """
-    task_results = GroupResult(group_dir).get_results()
-    # Read accuracies from each file and return list of values in tuples
-    return [_read_task_values(task_result) for task_result in task_results]
-
-
-def build_plot_values(values: Tuple[bool, str, List[float], int]) -> PlotValues:
-    """Returns PlotValues with appropriate indices based on task class (Unitary or Federated)"""
-    is_unitary, task_label, task_accuracies, E = values
-
-    if is_unitary:
-        indices = [i for i in range(1, len(task_accuracies) + 1, 1)]
-    else:
-        indices = [i for i in range(E, len(task_accuracies) * E + 1, E)]
-
-    return (task_label, task_accuracies, indices)
-
-
-def _prepare_aggregation_data(group_name: str) -> List[PlotValues]:
-    """Constructs and returns curves and xticks_args
-
-    Args:
-        group_name (str): group name for which to construct the curves
-
-    Returns:
-        A list of `PlotValues`.
-    """
-    group_dir = os.path.join(FLAGS.results_dir, group_name)
-    # List of tuples (benchmark_name, unitary_accuracy, federated_accuracy)
-    values = read_all_task_values(group_dir=group_dir)
-
-    assert values, "No values for group found"
-
-    data: List[PlotValues] = list(map(build_plot_values, values))
-
-    return data
-
-
-def aggregate() -> str:
-    """Plots task accuracies for all federated tasks in a group
-    Expects FLAGS.group_name to be set
-
-    Returns:
-        str: Absolut path to saved plot
-    """
-    group_name = FLAGS.group_name
-    dname = storage.create_output_subdir(group_name)
-    fname = storage.fname_with_default_dir("plot_task_accuracies.png", dname)
-
-    data = _prepare_aggregation_data(group_name)
-
-    # Take highest length of values list as xlim_max
-    xlim_max = max([len(values) for _, values, _ in data]) + 1
-
-    fpath = plot(
-        data,
-        title="",  # TODO
-        ylabel="Accuracy",
-        fname=fname,
-        ylim_max=1.0,
-        xlim_max=xlim_max,
-    )
-
-    logger.info("Data plotted and saved in file", filepath=fpath)
-
-    return fpath
diff --git a/benchmarks/benchmark/aggregation/task_accuracies_test.py b/benchmarks/benchmark/aggregation/task_accuracies_test.py
deleted file mode 100644
index b9e47d2cf..000000000
--- a/benchmarks/benchmark/aggregation/task_accuracies_test.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import os
-
-import pytest
-
-from xain_fl.helpers import sha1
-
-from . import task_accuracies
-
-
-@pytest.mark.integration
-def test_plot_task_accuracies(output_dir, group_name, monkeypatch):
-    # Prepare
-    data = [
-        (
-            "unitary",
-            [0.96, 0.90, 0.81, 0.72, 0.63, 0.54, 0.45, 0.36, 0.27, 0.18, 0.09],
-            range(1, 12, 1),
-        ),
-        (
-            "federated",
-            [0.92, 0.89, 0.87, 0.85, 0.83, 0.81, 0.80, 0.79, 0.78, 0.77, 0.77],
-            range(1, 12, 1),
-        ),
-    ]
-    fname = f"plot_task_accuracies.png"
-    expected_filepath = os.path.join(output_dir, group_name, fname)
-    expected_sha1 = "54b9c71e6890fa3083029c88544a51ca4a2ad480"
-
-    def mock_prepare_aggregation_data(_: str):
-        return data
-
-    monkeypatch.setattr(
-        task_accuracies, "_prepare_aggregation_data", mock_prepare_aggregation_data
-    )
-
-    # Execute
-    actual_filepath = task_accuracies.aggregate()
-
-    # If any error occurs we will be able to look at the plot. If the the ploting
-    # logic is changed the file under this path can be used to get the new hash
-    # after evaluating the rendered plot
-    print(actual_filepath)
-
-    # Assert
-    assert expected_filepath == actual_filepath
-    assert expected_sha1 == sha1.checksum(actual_filepath), "Checksum not matching"
diff --git a/benchmarks/benchmark/bench_cl.py b/benchmarks/benchmark/bench_cl.py
deleted file mode 100644
index 544e4ed09..000000000
--- a/benchmarks/benchmark/bench_cl.py
+++ /dev/null
@@ -1,112 +0,0 @@
-"""Experimental"""
-import time
-
-import tensorflow as tf
-from absl import app
-
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-
-from .exec import run
-
-logger = get_logger(__name__)
-
-B = 64
-
-
-def bench_cl90(_):
-    """
-    This benchmark compares training on the full (original) Fashion-MNIST dataset using
-    (a) the participant directly and (b) the coordinator with only one participant.
-    Results should not vary apart from the expected non-determinism during training.
-    """
-    logger.info("Starting CL on full Fashion-MNIST")
-    # Load original Fashion-MNIST
-    xy_train, xy_val, xy_test = data(limit=False)
-    bench_cl_ul(
-        "cl90-using-participant",
-        xy_train,
-        xy_val,
-        xy_test,
-        epochs=10,
-        use_coordinator=False,
-    )
-    bench_cl_ul(
-        "cl90-using-coordinator",
-        xy_train,
-        xy_val,
-        xy_test,
-        epochs=10,
-        use_coordinator=True,
-    )
-
-
-def bench_ul80(_):
-    """
-    This benchmark compares training on a subset (550 examples) of the original
-    Fashion-MNIST dataset using (a) the participant directly and (b) the coordinator with
-    only one participant. Results should not vary apart from the usual non-determinism
-    during training.
-    """
-    logger.info("Starting UL on 550-example Fashion-MNIST subset")
-    # Load original Fashion-MNIST subset
-    xy_train, xy_val, xy_test = data(limit=True)
-    bench_cl_ul(
-        "ul80-using-participant",
-        xy_train,
-        xy_val,
-        xy_test,
-        epochs=100,
-        use_coordinator=False,
-    )
-    bench_cl_ul(
-        "ul80-using-coordinator",
-        xy_train,
-        xy_val,
-        xy_test,
-        epochs=100,
-        use_coordinator=True,
-    )
-
-
-def bench_cl_ul(
-    name: str, xy_train, xy_val, xy_test, epochs: int, use_coordinator: bool
-):
-    start = time.time()
-    if use_coordinator:
-        hist, _, _, _, loss, acc = run.federated_training(
-            "blog_cnn", [xy_train], xy_val, xy_test, R=epochs, E=1, C=0, B=B
-        )
-    else:
-        hist, loss, acc = run.unitary_training(
-            "blog_cnn", xy_train, xy_val, xy_test, E=epochs, B=B
-        )
-    end = time.time()
-
-    # Write results JSON
-    results = {
-        "name": name,
-        "start": start,
-        "end": end,
-        "duration": end - start,
-        "E": epochs,
-        "B": B,
-        "unitary_learning": {"loss": float(loss), "acc": float(acc), "hist": hist},
-    }
-    storage.write_json(results, fname=name + "-results.json")
-
-
-def data(limit: bool):
-    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
-    # Split xy_val
-    (x_train, x_valid) = x_train[5000:], x_train[:5000]
-    (y_train, y_valid) = y_train[5000:], y_train[:5000]
-    # Subset
-    if limit:
-        x_train = x_train[:550]
-        y_train = y_train[:550]
-    return (x_train, y_train), (x_valid, y_valid), (x_test, y_test)
-
-
-if __name__ == "__main__":
-    app.run(main=bench_ul80)
diff --git a/benchmarks/benchmark/bench_ea.py b/benchmarks/benchmark/bench_ea.py
deleted file mode 100644
index 6a2af83fe..000000000
--- a/benchmarks/benchmark/bench_ea.py
+++ /dev/null
@@ -1,76 +0,0 @@
-"""Experimental"""
-from absl import app
-
-from benchmarks.benchmark.exec import run
-from benchmarks.benchmark.net import orig_cnn_compiled
-from benchmarks.helpers import storage
-from xain_fl.datasets import load_splits
-from xain_fl.fl.coordinator.aggregate import EvoAgg
-from xain_fl.fl.coordinator.evaluator import Evaluator
-from xain_fl.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-DEFAULT_R = 50
-DEFAULT_E = 1  # Number of training epochs in each round
-DEFAULT_C = 0.3  # Fraction of participants used in each round of training
-DEFAULT_B = 32  # Batch size used by participants
-
-
-def benchmark_evolutionary_avg():
-    fn_name = benchmark_evolutionary_avg.__name__
-    logger.info("Starting {}".format(fn_name))
-
-    # Load dataset
-    xy_parts, xy_val, xy_test = load_splits("fashion-mnist-100p-noniid-03cpp")
-
-    # Run Federated Learning with evolutionary aggregation
-    evaluator = Evaluator(orig_cnn_compiled(), xy_val)  # FIXME refactor
-    aggregator = EvoAgg(evaluator)
-    _, _, _, _, loss_a, acc_a = run.federated_training(
-        "blog_cnn",
-        xy_parts,
-        xy_val,
-        xy_test,
-        R=DEFAULT_R,
-        E=DEFAULT_E,
-        C=DEFAULT_C,
-        B=DEFAULT_B,
-        aggregator=aggregator,
-    )
-
-    # Run Federated Learning with weighted average aggregation
-    _, _, _, _, loss_b, acc_b = run.federated_training(
-        "blog_cnn",
-        xy_parts,
-        xy_val,
-        xy_test,
-        R=DEFAULT_R,
-        E=DEFAULT_E,
-        C=DEFAULT_C,
-        B=DEFAULT_B,
-    )
-
-    # Write results JSON
-    results = {}
-    results["loss_a"] = float(loss_a)
-    results["acc_a"] = float(acc_a)
-    results["loss_b"] = float(loss_b)
-    results["acc_b"] = float(acc_b)
-    # TODO add histories
-    storage.write_json(results, fname="EA-WA-results.json")
-
-
-def benchmark_evolutionary_avg_with_noise():
-    fn_name = benchmark_evolutionary_avg.__name__
-    logger.info("Starting {}".format(fn_name))
-    raise NotImplementedError()
-
-
-def main(_):
-    benchmark_evolutionary_avg()
-
-
-if __name__ == "__main__":
-    app.run(main=main)
diff --git a/benchmarks/benchmark/benchmark.py b/benchmarks/benchmark/benchmark.py
deleted file mode 100644
index ea9aae7f3..000000000
--- a/benchmarks/benchmark/benchmark.py
+++ /dev/null
@@ -1,848 +0,0 @@
-"""Contains a set of benchmark scenarios expressed using a simple DSL.
-"""
-import os
-from tempfile import TemporaryDirectory
-from time import strftime
-from typing import Dict, List, Optional
-
-from absl import flags
-
-from benchmarks.helpers import storage
-from benchmarks.ops import docker, results, run
-from xain_fl.logger import get_logger
-
-from .task import Task, UnitaryVisionTask, VisionTask
-
-logger = get_logger(__name__)
-
-
-FLAGS = flags.FLAGS
-
-
-class Benchmark:
-    """DSL primitive used to represent a single benchmark scenario."""
-
-    def __init__(self, tasks: List[Task], aggregation_name: str, runner: str = "ec2"):
-        """Initializes Benchmark.
-
-        Args:
-            tasks (List[Task]): List of tasks to be performed as part of the benchmark
-            aggregation_name (str): One of the aggregation names in
-                ~benchmarks.aggregation.aggregation.aggregations
-            runner (str): One of "ec2" or "docker"
-        """
-        self.tasks = tasks
-        self.aggregation_name = aggregation_name
-        self.runner = runner
-
-
-benchmarks: Dict[str, Benchmark] = {
-    #
-    # ##############################
-    # UL/FL CPP
-    # ##############################
-    #
-    "flul-fashion-mnist-100p-iid-balanced": Benchmark(
-        tasks=[
-            UnitaryVisionTask(
-                name="unitary",
-                label="Unitary",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                E=100,
-                partition_id=0,
-                instance_cores=8,
-            ),
-            VisionTask(
-                name="federated",
-                label="Federated",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "flul-fashion-mnist-100p-noniid-05cpp": Benchmark(
-        tasks=[
-            UnitaryVisionTask(
-                name="unitary",
-                label="Unitary",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                E=100,
-                partition_id=0,
-                instance_cores=8,
-            ),
-            VisionTask(
-                name="federated",
-                label="Federated",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    #
-    # ##############################
-    # UL/FL VOL
-    # ##############################
-    #
-    "flul-fashion-mnist-100p-b1_045": Benchmark(
-        tasks=[
-            UnitaryVisionTask(
-                name="unitary_p0",
-                label='"Unitary (n=30)"',  # low volume
-                dataset_name="fashion-mnist-100p-b1_045",
-                model_name="orig_cnn",
-                E=100,
-                B=16,
-                partition_id=0,
-                instance_cores=16,
-                timeout=120,
-            ),
-            UnitaryVisionTask(
-                name="unitary_p99",
-                label='"Unitary (n=2356)"',  # high volume
-                dataset_name="fashion-mnist-100p-b1_045",
-                model_name="orig_cnn",
-                E=100,
-                B=16,
-                partition_id=99,
-                instance_cores=16,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated",
-                label="Federated",
-                dataset_name="fashion-mnist-100p-b1_045",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                B=16,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    #
-    # ##############################
-    # CPP CONTINUUM
-    # ##############################
-    #
-    "cpp-fashion-mnist-100p": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-01cpp",
-                label="01cpp",
-                dataset_name="fashion-mnist-100p-noniid-01cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-02cpp",
-                label="02cpp",
-                dataset_name="fashion-mnist-100p-noniid-02cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-03cpp",
-                label="03cpp",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-04cpp",
-                label="04cpp",
-                dataset_name="fashion-mnist-100p-noniid-04cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-05cpp",
-                label="05cpp",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-06cpp",
-                label="06cpp",
-                dataset_name="fashion-mnist-100p-noniid-06cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-07cpp",
-                label="07cpp",
-                dataset_name="fashion-mnist-100p-noniid-07cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-08cpp",
-                label="08cpp",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-09cpp",
-                label="09cpp",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            VisionTask(
-                name="federated-balanced",
-                label="balanced",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=100,
-                E=1,
-                instance_cores=32,
-                timeout=180,
-            ),
-            UnitaryVisionTask(
-                name="unitary-01cpp",
-                label="01cpp",
-                dataset_name="fashion-mnist-100p-noniid-01cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-02cpp",
-                label="02cpp",
-                dataset_name="fashion-mnist-100p-noniid-02cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-03cpp",
-                label="03cpp",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-04cpp",
-                label="04cpp",
-                dataset_name="fashion-mnist-100p-noniid-04cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-05cpp",
-                label="05cpp",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-06cpp",
-                label="06cpp",
-                dataset_name="fashion-mnist-100p-noniid-06cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-07cpp",
-                label="07cpp",
-                dataset_name="fashion-mnist-100p-noniid-07cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-08cpp",
-                label="08cpp",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-09cpp",
-                label="09cpp",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-            UnitaryVisionTask(
-                name="unitary-balanced",
-                label="balanced",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                E=100,
-                instance_cores=16,
-            ),
-        ],
-        aggregation_name="cpp-aggregation",
-    ),
-    #
-    # ##############################
-    # VOL CONTINUUM
-    # ##############################
-    #
-    ## TODO
-    #
-    # ##############################
-    # C LOW-TO-HIGH
-    # ##############################
-    #
-    "C-fashion-mnist-100p-noniid-03cpp": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-C0_01",
-                label="C=0.01",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.01,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_03",
-                label="C=0.03",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.03,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_10",
-                label="C=0.1",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_20",
-                label="C=0.2",
-                dataset_name="fashion-mnist-100p-noniid-03cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.2,
-                instance_cores=32,
-                timeout=240,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "C-fashion-mnist-100p-noniid-05cpp": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-C0_01",
-                label="C=0.01",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.01,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_03",
-                label="C=0.03",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.03,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_10",
-                label="C=0.1",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_20",
-                label="C=0.2",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.2,
-                instance_cores=32,
-                timeout=240,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "C-fashion-mnist-100p-noniid-09cpp": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-C0_01",
-                label="C=0.01",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.01,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_03",
-                label="C=0.03",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.03,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_10",
-                label="C=0.1",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_20",
-                label="C=0.2",
-                dataset_name="fashion-mnist-100p-noniid-09cpp",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.2,
-                instance_cores=32,
-                timeout=240,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "C-fashion-mnist-100p-iid-balanced": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-C0_01",
-                label="C=0.01",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.01,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_03",
-                label="C=0.03",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.03,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_10",
-                label="C=0.1",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-C0_20",
-                label="C=0.2",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=50,
-                E=1,
-                C=0.2,
-                instance_cores=32,
-                timeout=240,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    #
-    # ##############################
-    # E LOW-TO-HIGH
-    # ##############################
-    #
-    "E-fashion-mnist-100p-iid-balanced": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-E01",
-                label="E=01",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=128,
-                E=1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E02",
-                label="E=02",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=64,
-                E=2,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E04",
-                label="E=04",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=32,
-                E=4,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E08",
-                label="E=08",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=16,
-                E=8,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E16",
-                label="E=16",
-                dataset_name="fashion-mnist-100p-iid-balanced",
-                model_name="orig_cnn",
-                R=8,
-                E=16,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "E-fashion-mnist-100p-noniid-08cpp": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-E01",
-                label="E=01",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=128,
-                E=1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E02",
-                label="E=02",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=64,
-                E=2,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E04",
-                label="E=04",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=32,
-                E=4,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E08",
-                label="E=08",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=16,
-                E=8,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E16",
-                label="E=16",
-                dataset_name="fashion-mnist-100p-noniid-08cpp",
-                model_name="orig_cnn",
-                R=8,
-                E=16,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "E-fashion-mnist-100p-noniid-05cpp": Benchmark(
-        tasks=[
-            VisionTask(
-                name="federated-E01",
-                label="E=01",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=128,
-                E=1,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E02",
-                label="E=02",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=64,
-                E=2,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E04",
-                label="E=04",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=32,
-                E=4,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E08",
-                label="E=08",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=16,
-                E=8,
-                instance_cores=32,
-                timeout=120,
-            ),
-            VisionTask(
-                name="federated-E16",
-                label="E=16",
-                dataset_name="fashion-mnist-100p-noniid-05cpp",
-                model_name="orig_cnn",
-                R=8,
-                E=16,
-                instance_cores=32,
-                timeout=120,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    #
-    # ##############################
-    # End-To-End Tests
-    # ##############################
-    #
-    "e2e-flul-fashion-mnist": Benchmark(
-        tasks=[
-            UnitaryVisionTask(
-                name="unitary",
-                dataset_name="fashion-mnist-100p-noniid-02cpp",
-                E=4,
-                partition_id=0,
-                instance_cores=4,
-                timeout=10,
-            ),
-            VisionTask(
-                name="federated",
-                dataset_name="fashion-mnist-100p-noniid-02cpp",
-                R=2,
-                E=2,
-                C=0.02,
-                instance_cores=4,
-                timeout=10,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-    "e2e-flul-cifar-10": Benchmark(
-        tasks=[
-            UnitaryVisionTask(
-                name="unitary",
-                dataset_name="cifar-10-100p-noniid-02cpp",
-                E=4,
-                partition_id=0,
-                instance_cores=16,
-                timeout=10,
-            ),
-            VisionTask(
-                name="federated",
-                dataset_name="cifar-10-100p-noniid-02cpp",
-                R=2,
-                E=2,
-                C=0.02,
-                instance_cores=16,
-                timeout=10,
-            ),
-        ],
-        aggregation_name="flul-aggregation",
-    ),
-}
-
-
-def _run_benchmark(benchmark_name: str):
-    logger.info("Building Docker image for benchmark", benchmark_name=benchmark_name)
-    logger.info("Starting benchmark", benchmark_name=benchmark_name)
-
-    benchmark = benchmarks[benchmark_name]
-
-    group_name = FLAGS.group_name or f"{strftime('%Y%m%dT%H%M')}_{benchmark_name}"
-
-    task_names = {task.name for task in benchmark.tasks}
-
-    assert len(task_names) == len(benchmark.tasks), "Duplicate task names"
-
-    should_push = benchmark.runner == "ec2"
-    docker_image_name = docker.build(should_push=should_push)
-
-    # TODO Initiate tasks in parallel
-    for task in benchmark.tasks:
-        model_name = task.model_name
-        dataset_name = task.dataset_name
-        _run_task(
-            docker_image_name=docker_image_name,
-            group_name=group_name,
-            task_name=task.name,
-            task_label=task.label,
-            model=model_name,
-            dataset=dataset_name,
-            R=task.R,
-            E=task.E,
-            C=task.C,
-            B=task.B,
-            partition_id=task.partition_id,
-            instance_cores=task.instance_cores,
-            timeout=task.timeout,
-            runner=benchmark.runner,
-        )
-
-    with TemporaryDirectory() as tmpdir:
-        fname = os.path.join(tmpdir, "config.json")
-        data = {"aggregation_name": benchmark.aggregation_name}
-        storage.write_json(data, fname)
-        results.push(group_name=group_name, task_name="", output_dir=tmpdir)
-
-
-def _run_task(
-    docker_image_name: str,
-    group_name: str,
-    task_name: str,
-    task_label: str,
-    model: str,
-    dataset: str,
-    R: int,
-    E: int,
-    C: float,
-    B: int,
-    partition_id: Optional[int],
-    instance_cores: int,
-    timeout: int,
-    runner: str,  # one of ["ec2", "docker"]
-):
-    task_msg = f"{model}, {dataset}, {R}, {E}, {C}, {B}, {instance_cores}, {timeout}"
-    logger.info("Attempting to run task", runner=runner, task_msg=task_msg)
-
-    if runner == "ec2":
-        r = run.ec2
-    elif runner == "docker":
-        r = run.docker
-    else:
-        raise Exception("Runner does not exist")
-
-    r(
-        image=docker_image_name,
-        timeout=timeout,
-        instance_cores=instance_cores,
-        # The following arguments will be passed as absl flags:
-        group_name=group_name,
-        task_name=task_name,
-        task_label=task_label,
-        model=model,
-        dataset=dataset,
-        R=R,
-        E=E,
-        C=C,
-        B=B,
-        partition_id=partition_id,
-    )
-
-
-def main(_):
-    """Used by ~benchmarks.train_remote.main to start a benchmark identified by
-        commandline flag `--benchmark_name`. Has to be invoked through abseil `app.run`.
-    """
-    benchmark_name = FLAGS.benchmark_name
-    assert benchmark_name in benchmarks
-    _run_benchmark(benchmark_name=benchmark_name)
diff --git a/benchmarks/benchmark/benchmark_test.py b/benchmarks/benchmark/benchmark_test.py
deleted file mode 100644
index 8e8f9a2ff..000000000
--- a/benchmarks/benchmark/benchmark_test.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from benchmarks.ops.run import cores
-from xain_fl.datasets.dataset import config
-
-from .aggregation.aggregation import aggregations
-from .benchmark import benchmarks
-from .net import model_fns
-
-
-def test_valid_aggregation_names():
-    """
-    Verify that all aggregation names used in `Benchmark` objects are available in `aggregations`
-    """
-
-    # Prepare
-    aggregation_names = [benchmarks[b].aggregation_name for b in benchmarks]
-
-    # Assert
-    for aggregation_name in aggregation_names:
-        assert aggregation_name in aggregations
-
-
-def test_valid_model_names():
-    """
-    Verify that all model names used in `Task` objects are available in `model_fns`
-    """
-
-    # Prepare
-    model_names = [task.model_name for b in benchmarks for task in benchmarks[b].tasks]
-
-    # Assert
-    for model_name in model_names:
-        assert model_name in model_fns
-
-
-def test_valid_dataset_names():
-    """
-    Verify that all dataset names used in `Task` objects are available in `xain_fl.datasets`
-    """
-
-    # Prepare
-    dataset_names = [
-        task.dataset_name for b in benchmarks for task in benchmarks[b].tasks
-    ]
-    all_datasets = config.keys()
-
-    # Assert
-    for dataset_name in dataset_names:
-        assert dataset_name in all_datasets
-
-
-def test_valid_instance_cores():
-    """
-    Verify that all dataset names used in `Task` objects are available in `xain_fl.datasets`
-    """
-
-    # Prepare
-    instance_cores = [
-        task.instance_cores for b in benchmarks for task in benchmarks[b].tasks
-    ]
-
-    # Assert
-    for ic in instance_cores:
-        assert ic in cores
diff --git a/benchmarks/benchmark/conftest.py b/benchmarks/benchmark/conftest.py
deleted file mode 100644
index 2603774b9..000000000
--- a/benchmarks/benchmark/conftest.py
+++ /dev/null
@@ -1,21 +0,0 @@
-import pytest
-from absl import flags
-
-FLAGS = flags.FLAGS
-
-
-@pytest.fixture
-def group_name():
-    gname = "foo_bar_group"
-    FLAGS(["test", f"--group_name={gname}"])
-
-    return gname
-
-
-@pytest.fixture
-def results_dir(tmpdir):
-    """Create a results_dir containing one file in a subdirectory"""
-    tmpdir = str(tmpdir)
-    FLAGS(["test", f"--results_dir={tmpdir}"])
-
-    return tmpdir
diff --git a/benchmarks/benchmark/exec/__init__.py b/benchmarks/benchmark/exec/__init__.py
deleted file mode 100644
index b60708fa2..000000000
--- a/benchmarks/benchmark/exec/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-from absl import flags
-
-from benchmarks.benchmark.net import model_fns
-
-flags.DEFINE_string(
-    "task_name",
-    None,
-    "Mainly used for directory names and as a reference. If label not given used also for plots",
-)
-
-flags.DEFINE_string("task_label", None, "Label to be used in plots")
-
-flags.DEFINE_string("model", None, f"Model name, one of {[fn for fn in model_fns]}")
-
-flags.DEFINE_string("dataset", None, "Dataset name")
-
-flags.DEFINE_integer("R", None, "Rounds of federated learning")
-
-flags.DEFINE_integer("E", None, "Epochs of training in each round")
-
-flags.DEFINE_float("C", None, "Fraction of participants participating in each round")
-
-flags.DEFINE_integer("B", None, "Batch size")
-
-flags.DEFINE_integer("partition_id", None, "Partition ID for unitary training")
-
-flags.DEFINE_bool("push_results", True, "Indicates if results should be pushed to S3")
diff --git a/benchmarks/benchmark/exec/__main__.py b/benchmarks/benchmark/exec/__main__.py
deleted file mode 100644
index 4b1b65dac..000000000
--- a/benchmarks/benchmark/exec/__main__.py
+++ /dev/null
@@ -1,97 +0,0 @@
-import atexit
-import time
-
-from absl import app, flags
-
-from benchmarks.helpers import storage
-from benchmarks.ops import results
-from xain_fl.datasets import load_splits
-
-from . import run
-
-FLAGS = flags.FLAGS
-
-
-def after_main(group_name: str, task_name: str):
-    """Will run after main exists (successfully or otherwise)"""
-    # Push results once task has finished
-    results.push(group_name=group_name, task_name=task_name)
-
-
-def main(_):
-    # Set exit callback
-    if FLAGS.push_results:
-        atexit.register(
-            after_main, group_name=FLAGS.group_name, task_name=FLAGS.task_name
-        )
-
-    # Load data
-    xy_train_partitions, xy_val, xy_test = load_splits(FLAGS.dataset)
-
-    # Execute training
-    start = time.time()
-    partition_id = FLAGS.partition_id
-
-    num_participants = 1  # For unitary training
-    hist_opt_configs = None  # For unitary training
-    hist_metrics = None  # For unitary training
-
-    if partition_id is not None:  # Use only a single partition if required (unitary)
-        hist, loss, acc = run.unitary_training(
-            model_name=FLAGS.model,
-            xy_train=xy_train_partitions[partition_id],
-            xy_val=xy_val,
-            xy_test=xy_test,
-            E=FLAGS.E,
-            B=FLAGS.B,
-        )
-    else:
-        hist, _, hist_opt_configs, hist_metrics, loss, acc = run.federated_training(
-            model_name=FLAGS.model,
-            xy_train_partitions=xy_train_partitions,
-            xy_val=xy_val,
-            xy_test=xy_test,
-            R=FLAGS.R,
-            E=FLAGS.E,
-            C=FLAGS.C,
-            B=FLAGS.B,
-        )
-        num_participants = len(xy_train_partitions)
-    end = time.time()
-
-    # Write results
-    res = {
-        "group_name": FLAGS.group_name,
-        "task_name": FLAGS.task_name,
-        "task_label": FLAGS.task_label,
-        "dataset": FLAGS.dataset,
-        "model": FLAGS.model,
-        "R": FLAGS.R,
-        "E": FLAGS.E,
-        "C": FLAGS.C,
-        "B": FLAGS.B,
-        "partition_id": partition_id,
-        "start": start,
-        "end": end,
-        "duration": end - start,
-        "loss": float(loss),
-        "acc": float(acc),
-        "hist": hist,
-        "hist_opt_configs": hist_opt_configs,
-        "hist_metrics": hist_metrics,
-        "num_participants": num_participants,
-    }
-    storage.write_json(res, fname="results.json")
-
-
-if __name__ == "__main__":
-    flags.mark_flag_as_required("group_name")
-    flags.mark_flag_as_required("task_name")
-    flags.mark_flag_as_required("model")
-    flags.mark_flag_as_required("dataset")
-    flags.mark_flag_as_required("R")
-    flags.mark_flag_as_required("E")
-    flags.mark_flag_as_required("C")
-    flags.mark_flag_as_required("B")
-    # Note: Flag partition_id is not required (i.e. optional)
-    app.run(main=main)
diff --git a/benchmarks/benchmark/exec/run.py b/benchmarks/benchmark/exec/run.py
deleted file mode 100644
index 51c12bd9f..000000000
--- a/benchmarks/benchmark/exec/run.py
+++ /dev/null
@@ -1,198 +0,0 @@
-import random
-import time
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-import tensorflow as tf
-from absl import logging
-
-from benchmarks.benchmark.aggregation import task_accuracies
-from benchmarks.benchmark.net import load_lr_fn_fn, load_model_fn
-from benchmarks.helpers import storage
-from xain_fl.datasets import load_splits
-from xain_fl.fl.coordinator import Coordinator, RandomController
-from xain_fl.fl.coordinator.aggregate import Aggregator
-from xain_fl.fl.participant import ModelProvider, Participant
-from xain_fl.types import History, Metrics, Partition
-
-random.seed(0)
-np.random.seed(1)
-tf.compat.v1.set_random_seed(2)
-
-
-# Default parameters for `unitary_versus_federated`
-DEFAULT_R = 50  # Number of total rounds to train
-DEFAULT_E = 4  # Number of training epochs in each round
-DEFAULT_C = 0.1  # Fraction of participants used in each round of training
-DEFAULT_B = 64  # Batch size used by participants
-
-
-# pylint: disable-msg=too-many-locals,too-many-arguments
-def unitary_training(
-    model_name: str,
-    xy_train: Partition,
-    xy_val: Partition,
-    xy_test: Partition,
-    E: int,
-    B: int,
-) -> Tuple[History, float, float]:
-
-    model_fn = load_model_fn(model_name)
-    lr_fn_fn = load_lr_fn_fn(model_name)
-    model_provider = ModelProvider(model_fn=model_fn, lr_fn_fn=lr_fn_fn)
-
-    # Initialize model and participant
-    cid = 0
-    participant = Participant(
-        cid,
-        model_provider,
-        xy_train=xy_train,
-        xy_val=xy_val,
-        num_classes=10,
-        batch_size=B,
-    )
-    model = model_provider.init_model()
-    theta = model.get_weights()
-
-    # Train model
-    hist = participant._fit(model, E, [])  # pylint: disable-msg=protected-access
-
-    # Evaluate final performance
-    theta = model.get_weights()
-    loss, acc = participant.evaluate(theta, xy_test)
-
-    # Report results
-    return hist, loss, acc
-
-
-# pylint: disable-msg=too-many-locals,too-many-arguments
-def federated_training(
-    model_name: str,
-    xy_train_partitions: List[Partition],
-    xy_val: Partition,
-    xy_test: Partition,
-    R: int,
-    E: int,
-    C: float,
-    B: int,
-    aggregator: Aggregator = None,
-) -> Tuple[
-    History, List[List[History]], List[List[Dict]], List[List[Metrics]], float, float
-]:
-    # Initialize participants and coordinator
-    # Note that there is no need for common initialization at this point: Common
-    # initialization will happen during the first few rounds because the coordinator will
-    # push its own weight to the respective participants of each training round.
-
-    model_fn = load_model_fn(model_name)
-    lr_fn_fn = load_lr_fn_fn(model_name)
-    model_provider = ModelProvider(model_fn=model_fn, lr_fn_fn=lr_fn_fn)
-
-    # Init participants
-    participants = []
-    for cid, xy_train in enumerate(xy_train_partitions):
-        participant = Participant(
-            cid, model_provider, xy_train, xy_val, num_classes=10, batch_size=B
-        )
-        participants.append(participant)
-    participant_ids = [str(participant.cid) for participant in participants]
-
-    # Init coordinator
-    controller = RandomController(participant_ids, fraction_of_participants=C)
-    coordinator = Coordinator(
-        controller,
-        model_provider,
-        participants,
-        C=C,
-        E=E,
-        xy_val=xy_val,
-        aggregator=aggregator,
-    )
-
-    # Train model
-    hist_co, hist_ps, hist_opt_configs, hist_metrics = coordinator.fit(num_rounds=R)
-
-    # Evaluate final performance
-    loss, acc = coordinator.evaluate(xy_test)
-
-    # Report results
-    return hist_co, hist_ps, hist_opt_configs, hist_metrics, loss, acc
-
-
-# FIXME remove
-def unitary_versus_federated(
-    benchmark_name: str,
-    model_name: str,
-    dataset_name: str,
-    R: int = DEFAULT_R,
-    E: int = DEFAULT_E,
-    C: float = DEFAULT_C,
-    B: int = DEFAULT_B,
-):
-    """
-    :param C: Fraction of participants used in each round of training
-    """
-    logging.info("Starting benchmark", benchmark_name=benchmark_name)
-
-    xy_train_partitions, xy_val, xy_test = load_splits(dataset_name)
-
-    start = time.time()
-
-    # Train CNN on a single partition ("unitary learning")
-    # TODO train n models on all partitions
-    partition_id = 0
-    xy_train = xy_train_partitions[partition_id]
-
-    logging.info("Run unitary training using partition", partition_id=partition_id)
-
-    ul_hist, ul_loss, ul_acc = unitary_training(
-        model_name, xy_train, xy_val, xy_test, E=R * E, B=B
-    )
-
-    # Train CNN using federated learning on all partitions
-    logging.info("Run federated learning using all partitions")
-    fl_hist, _, _, _, fl_loss, fl_acc = federated_training(
-        model_name, xy_train_partitions, xy_val, xy_test, R=R, E=E, C=C, B=B
-    )
-
-    end = time.time()
-
-    # Write results JSON
-    results = {
-        "name": benchmark_name,
-        "start": start,
-        "end": end,
-        "duration": end - start,
-        "R": R,
-        "E": E,
-        "C": C,
-        "B": B,
-        "unitary_learning": {
-            "loss": float(ul_loss),
-            "acc": float(ul_acc),
-            "hist": ul_hist,
-        },
-        "federated_learning": {
-            "loss": float(fl_loss),
-            "acc": float(fl_acc),
-            "hist": fl_hist,
-        },
-    }
-    storage.write_json(results, fname="results.json")
-
-    # Plot results
-    # TODO include aggregated participant histories in plot
-    plot_data: List[Tuple[str, List[float], Optional[List[int]]]] = [
-        (
-            "Unitary Learning",
-            ul_hist["val_acc"],
-            [i for i in range(1, len(ul_hist["val_acc"]) + 1, 1)],
-        ),
-        (
-            "Federated Learning",
-            fl_hist["val_acc"],
-            [i for i in range(E, len(fl_hist["val_acc"]) * E + 1, E)],
-        ),
-    ]
-    # FIXME use different filenames for different datasets
-    task_accuracies.plot(plot_data, fname="plot.png")
diff --git a/benchmarks/benchmark/net/__init__.py b/benchmarks/benchmark/net/__init__.py
deleted file mode 100644
index aead2aa8b..000000000
--- a/benchmarks/benchmark/net/__init__.py
+++ /dev/null
@@ -1,53 +0,0 @@
-"""Provides implementations of a few standard neural network architectures and associated
-learning rate schedules.
-"""
-from typing import Callable, Dict
-
-import tensorflow as tf
-
-from .blog_cnn import blog_cnn_compiled, blog_cnn_lr_fn
-from .orig_2nn import orig_2nn_compiled, orig_2nn_lr_fn
-from .orig_cnn import orig_cnn_compiled, orig_cnn_lr_fn
-from .resnet import resnet20v2_compiled, resnet20v2_lr_fn
-
-model_fns: Dict[str, Callable[[], tf.keras.Model]] = {
-    "orig_2nn": orig_2nn_compiled,
-    "orig_cnn": orig_cnn_compiled,
-    "blog_cnn": blog_cnn_compiled,
-    "resnet20": resnet20v2_compiled,
-}
-
-lr_fns: Dict = {
-    "orig_2nn": orig_2nn_lr_fn,
-    "orig_cnn": orig_cnn_lr_fn,
-    "blog_cnn": blog_cnn_lr_fn,
-    "resnet20": resnet20v2_lr_fn,
-}
-
-
-def load_model_fn(model_name: str) -> Callable[[], tf.keras.Model]:
-    """Returns a function which can be used to create a Keras model.
-
-    Args:
-        model_name (str): One of ~benchmarks.benchmark.net.model_fns
-
-    Returns:
-        Callable[[], tf.keras.Model]
-    """
-    assert (
-        model_name in model_fns
-    ), f"Model name '{model_name}' not in {model_fns.keys()}"
-    return model_fns[model_name]
-
-
-def load_lr_fn_fn(model_name: str) -> Callable:
-    """Returns a function which can be used to obtain a learning rate schedule.
-
-    Args:
-        model_name (str): One of ~benchmarks.benchmark.net.lr_fns
-
-    Returns:
-        Callable
-    """
-    assert model_name in lr_fns, f"Model name '{model_name}' not in {lr_fns.keys()}"
-    return lr_fns[model_name]
diff --git a/benchmarks/benchmark/net/__main__.py b/benchmarks/benchmark/net/__main__.py
deleted file mode 100644
index 272b16a74..000000000
--- a/benchmarks/benchmark/net/__main__.py
+++ /dev/null
@@ -1,41 +0,0 @@
-from absl import app, logging
-
-from .orig_2nn import orig_2nn_compiled
-from .orig_cnn import orig_cnn_compiled
-from .resnet import resnet20v2_compiled
-
-
-def main(_):
-    print_model_summaries()
-
-
-def print_model_summaries():
-    logging.info("\nOrig. 2NN:")
-    model = orig_2nn_compiled()
-    model.summary(print_fn=logging.info)
-    print_weight_dims(model)
-
-    logging.info("\nOrig. CNN (MNIST):")
-    model = orig_cnn_compiled()
-    model.summary(print_fn=logging.info)
-    print_weight_dims(model)
-
-    logging.info("\nOrig. CNN (CIFAR-10):")
-    model = orig_cnn_compiled(input_shape=(32, 32, 3))
-    model.summary(print_fn=logging.info)
-    print_weight_dims(model)
-
-    logging.info("\nResNet20v2 (CIFAR-10):")
-    model = resnet20v2_compiled(input_shape=(32, 32, 3))
-    model.summary(print_fn=logging.info)
-    print_weight_dims(model)
-
-
-def print_weight_dims(model):
-    weights = model.get_weights()
-    for w in weights:
-        print(w.shape)
-
-
-if __name__ == "__main__":
-    app.run(main=main)
diff --git a/benchmarks/benchmark/net/blog_cnn.py b/benchmarks/benchmark/net/blog_cnn.py
deleted file mode 100644
index be67fb909..000000000
--- a/benchmarks/benchmark/net/blog_cnn.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from typing import Callable
-
-import tensorflow as tf
-from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPool2D
-
-from . import lr_schedule
-
-# Derived from:
-# https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a
-
-DEFAULT_LR = 0.001
-DEFAULT_K = 0.025
-
-
-# pylint: disable-msg=unused-argument
-def blog_cnn_compiled(
-    input_shape=(28, 28, 1),
-    num_classes: int = 10,
-    lr_initial: float = DEFAULT_LR,
-    k: float = DEFAULT_K,
-    momentum: float = 0.9,
-    epoch_base: int = 0,
-    seed: int = 2017,
-) -> tf.keras.Model:
-    ki = tf.keras.initializers.glorot_uniform(seed=seed)
-
-    model = tf.keras.Sequential()
-    # Must define the input shape in the first layer of the neural network
-    model.add(
-        Conv2D(
-            filters=64,
-            kernel_size=2,
-            padding="same",
-            activation="relu",
-            kernel_initializer=ki,
-            input_shape=input_shape,
-        )
-    )
-    model.add(MaxPool2D(pool_size=2))
-    model.add(Dropout(0.3))
-    model.add(
-        Conv2D(
-            filters=32,
-            kernel_size=2,
-            padding="same",
-            activation="relu",
-            kernel_initializer=ki,
-        )
-    )
-    model.add(MaxPool2D(pool_size=2))
-    model.add(Dropout(0.3))
-    model.add(Flatten())
-    model.add(Dense(256, activation="relu", kernel_initializer=ki))
-    model.add(Dropout(0.5))
-    model.add(Dense(num_classes, activation="softmax", kernel_initializer=ki))
-
-    # Compile model with exponential learning rate decay
-    lr_fn = blog_cnn_lr_fn(epoch_base=epoch_base)
-    optimizer = tf.keras.optimizers.Adam(lr=lr_fn(0))
-
-    model.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-    )
-    return model
-
-
-def blog_cnn_lr_fn(
-    epoch_base: int, lr_initial: float = DEFAULT_LR, k: float = DEFAULT_K
-) -> Callable:
-    return lr_schedule.exp_decay_fn(epoch_base=epoch_base, lr_initial=lr_initial, k=k)
diff --git a/benchmarks/benchmark/net/blog_cnn_test.py b/benchmarks/benchmark/net/blog_cnn_test.py
deleted file mode 100644
index f8640f45d..000000000
--- a/benchmarks/benchmark/net/blog_cnn_test.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import random
-
-import numpy as np
-import pytest
-import tensorflow as tf
-
-from .blog_cnn import blog_cnn_compiled
-
-
-@pytest.mark.slow
-def test_num_parameters_mnist():
-    # Prepare
-    model = blog_cnn_compiled(input_shape=(28, 28, 1), num_classes=10)
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 412_778
-
-
-@pytest.mark.slow
-def test_num_parameters_cifar():
-    # Prepare
-    model = blog_cnn_compiled(input_shape=(32, 32, 3), num_classes=10)
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 536_170
-
-
-@pytest.mark.slow
-def test_seed_mnist():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = blog_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-    model_b = blog_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
-
-
-@pytest.mark.slow
-def test_seed_cifar():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = blog_cnn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-    model_b = blog_cnn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
-
-
-@pytest.mark.slow
-def test_seed_unequal():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-
-    # Execute
-    model_a = blog_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=3)
-    model_b = blog_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=4)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        assert ws_a[layer_i].shape == ws_b[layer_i].shape
-        if ws_a[layer_i].ndim == 1:
-            continue  # Bias can be the same
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert not np.any(np.equal(w_a, w_b))
diff --git a/benchmarks/benchmark/net/lr_schedule.py b/benchmarks/benchmark/net/lr_schedule.py
deleted file mode 100644
index 0c1972675..000000000
--- a/benchmarks/benchmark/net/lr_schedule.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import math
-from typing import Callable
-
-
-def exp_decay_fn(epoch_base: int, lr_initial: float, k: float) -> Callable:
-    def exp_decay(epoch_optimizer: int) -> float:
-        epoch = epoch_base + epoch_optimizer
-        return lr_initial * math.exp(-k * epoch)
-
-    return exp_decay
diff --git a/benchmarks/benchmark/net/orig_2nn.py b/benchmarks/benchmark/net/orig_2nn.py
deleted file mode 100644
index 3c406a192..000000000
--- a/benchmarks/benchmark/net/orig_2nn.py
+++ /dev/null
@@ -1,49 +0,0 @@
-from typing import Callable
-
-import tensorflow as tf
-from tensorflow.keras.layers import Dense, Flatten, Input
-
-from . import lr_schedule
-
-DEFAULT_LR = 0.01
-DEFAULT_K = 0.025
-
-
-# pylint: disable-msg=unused-argument
-def orig_2nn_compiled(
-    input_shape=(28, 28, 1),
-    num_classes=10,
-    lr_initial: float = DEFAULT_LR,
-    k: float = DEFAULT_K,
-    momentum: float = 0.0,
-    epoch_base: int = 0,
-    seed: int = 2017,
-) -> tf.keras.Model:
-    # Kernel initializer
-    ki = tf.keras.initializers.glorot_uniform(seed=seed)
-
-    # Architecture
-    inputs = Input(shape=input_shape)
-    x = Flatten()(inputs)
-    x = Dense(200, kernel_initializer=ki, activation="relu")(x)
-    x = Dense(200, kernel_initializer=ki, activation="relu")(x)
-    outputs = Dense(num_classes, kernel_initializer=ki, activation="softmax")(x)
-
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
-
-    # Compile model with exponential learning rate decay
-    lr_fn = orig_2nn_lr_fn(epoch_base=epoch_base)
-    optimizer = tf.keras.optimizers.SGD(lr=lr_fn(0), momentum=momentum)
-
-    model.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-    )
-    return model
-
-
-def orig_2nn_lr_fn(
-    epoch_base: int, lr_initial: float = DEFAULT_LR, k: float = DEFAULT_K
-) -> Callable:
-    return lr_schedule.exp_decay_fn(epoch_base=epoch_base, lr_initial=lr_initial, k=k)
diff --git a/benchmarks/benchmark/net/orig_2nn_test.py b/benchmarks/benchmark/net/orig_2nn_test.py
deleted file mode 100644
index e4ac928ba..000000000
--- a/benchmarks/benchmark/net/orig_2nn_test.py
+++ /dev/null
@@ -1,67 +0,0 @@
-import random
-
-import numpy as np
-import pytest
-import tensorflow as tf
-
-from .orig_2nn import orig_2nn_compiled
-
-
-@pytest.mark.slow
-def test_num_parameters():
-    # Prepare
-    model = orig_2nn_compiled()
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 199_210
-
-
-@pytest.mark.slow
-def test_seed_mnist():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = orig_2nn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-    model_b = orig_2nn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
-
-
-@pytest.mark.slow
-def test_seed_cifar():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = orig_2nn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-    model_b = orig_2nn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
diff --git a/benchmarks/benchmark/net/orig_cnn.py b/benchmarks/benchmark/net/orig_cnn.py
deleted file mode 100644
index 52406ee70..000000000
--- a/benchmarks/benchmark/net/orig_cnn.py
+++ /dev/null
@@ -1,67 +0,0 @@
-from typing import Callable
-
-import tensorflow as tf
-from tensorflow.keras.layers import Conv2D, Dense, Flatten, Input, MaxPool2D
-
-from . import lr_schedule
-
-DEFAULT_LR = 0.002
-DEFAULT_K = 0.01
-
-
-# pylint: disable-msg=unused-argument
-def orig_cnn_compiled(
-    input_shape=(28, 28, 1),
-    num_classes: int = 10,
-    lr_initial: float = DEFAULT_LR,
-    k: float = DEFAULT_K,
-    momentum: float = 0.0,
-    epoch_base: int = 0,
-    seed: int = 2017,
-) -> tf.keras.Model:
-    # Kernel initializer
-    ki = tf.keras.initializers.glorot_uniform(seed=seed)
-
-    # Architecture
-    inputs = Input(shape=input_shape)
-    x = Conv2D(
-        32,
-        kernel_size=(5, 5),
-        strides=(1, 1),
-        kernel_initializer=ki,
-        padding="same",
-        activation="relu",
-    )(inputs)
-    x = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)
-    x = Conv2D(
-        64,
-        kernel_size=(5, 5),
-        strides=(1, 1),
-        kernel_initializer=ki,
-        padding="same",
-        activation="relu",
-    )(x)
-    x = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)
-    x = Flatten()(x)
-    x = Dense(512, kernel_initializer=ki, activation="relu")(x)
-    outputs = Dense(num_classes, kernel_initializer=ki, activation="softmax")(x)
-
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
-
-    # Compile model with exponential learning rate decay
-    lr_fn = orig_cnn_lr_fn(epoch_base=epoch_base)
-    # optimizer = tf.keras.optimizers.SGD(lr=lr_fn(0), momentum=momentum)
-    optimizer = tf.keras.optimizers.Adam(lr=lr_fn(0))
-
-    model.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-    )
-    return model
-
-
-def orig_cnn_lr_fn(
-    epoch_base: int, lr_initial: float = DEFAULT_LR, k: float = DEFAULT_K
-) -> Callable:
-    return lr_schedule.exp_decay_fn(epoch_base=epoch_base, lr_initial=lr_initial, k=k)
diff --git a/benchmarks/benchmark/net/orig_cnn_test.py b/benchmarks/benchmark/net/orig_cnn_test.py
deleted file mode 100644
index 2d7ec8d85..000000000
--- a/benchmarks/benchmark/net/orig_cnn_test.py
+++ /dev/null
@@ -1,103 +0,0 @@
-import random
-
-import numpy as np
-import pytest
-import tensorflow as tf
-
-from .orig_cnn import orig_cnn_compiled
-
-
-@pytest.mark.slow
-def test_num_parameters_mnist():
-    # Prepare
-    model = orig_cnn_compiled(input_shape=(28, 28, 1), num_classes=10)
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 1_663_370
-
-
-@pytest.mark.slow
-def test_num_parameters_cifar():
-    # Prepare
-    model = orig_cnn_compiled(input_shape=(32, 32, 3), num_classes=10)
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 2_156_490
-
-
-@pytest.mark.slow
-def test_seed_mnist():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = orig_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-    model_b = orig_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
-
-
-@pytest.mark.slow
-def test_seed_cifar():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-    SEED = 3
-
-    # Execute
-    model_a = orig_cnn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-    model_b = orig_cnn_compiled(input_shape=(32, 32, 3), num_classes=10, seed=SEED)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert w_a.shape == w_b.shape
-        np.testing.assert_equal(w_a, w_b)
-
-
-@pytest.mark.slow
-def test_seed_unequal():
-    # Prepare
-    random.seed(0)
-    np.random.seed(1)
-    tf.set_random_seed(2)
-
-    # Execute
-    model_a = orig_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=3)
-    model_b = orig_cnn_compiled(input_shape=(28, 28, 1), num_classes=10, seed=4)
-
-    # Assert
-    assert model_a.count_params() == model_b.count_params()
-    # Ensure weight matrices are exactly the same
-    ws_a = model_a.get_weights()
-    ws_b = model_b.get_weights()
-    # pylint: disable-msg=consider-using-enumerate
-    for layer_i in range(len(ws_a)):
-        assert ws_a[layer_i].shape == ws_b[layer_i].shape
-        if ws_a[layer_i].ndim == 1:
-            continue  # Bias can be the same
-        w_a = ws_a[layer_i]
-        w_b = ws_b[layer_i]
-        assert not np.any(np.equal(w_a, w_b))
diff --git a/benchmarks/benchmark/net/resnet.py b/benchmarks/benchmark/net/resnet.py
deleted file mode 100644
index d9b6a2417..000000000
--- a/benchmarks/benchmark/net/resnet.py
+++ /dev/null
@@ -1,379 +0,0 @@
-# Derived from: https://github.com/keras-team/keras/blob/master/examples/cifar10_resnet.py
-# pylint: skip-file
-
-import math
-from typing import Callable, Optional, Tuple
-
-import tensorflow as tf
-import tensorflow.keras as keras
-from tensorflow.keras.layers import (
-    Activation,
-    AveragePooling2D,
-    BatchNormalization,
-    Conv2D,
-    Dense,
-    Dropout,
-    Flatten,
-    Input,
-)
-from tensorflow.keras.models import Model
-from tensorflow.keras.regularizers import l2
-
-from . import lr_schedule
-
-L2_DEFAULT: float = 1e-4
-KERNEL_INITIALIZER_DEFAULT = "he_normal"
-
-DEFAULT_LR = 0.1
-DEFAULT_K = 0.15
-
-
-def resnet20v2_compiled(
-    input_shape=(32, 32, 3),  # CIFAR
-    num_classes=10,
-    lr_initial: float = DEFAULT_LR,
-    k: float = DEFAULT_K,
-    momentum: float = 0.9,
-    epoch_base: int = 0,
-) -> tf.keras.Model:
-    model, _ = resnet(input_shape=(32, 32, 3), num_classes=10, version=2, n=2)
-
-    # Compile model with exponential learning rate decay
-    lr_fn = resnet20v2_lr_fn(epoch_base=epoch_base)
-    optimizer = tf.keras.optimizers.SGD(lr=lr_fn(0), momentum=momentum)
-
-    model.compile(
-        optimizer=optimizer,
-        loss=tf.keras.losses.categorical_crossentropy,
-        metrics=["accuracy"],
-    )
-    return model
-
-
-def resnet20v2_lr_fn(
-    epoch_base: int, lr_initial: float = DEFAULT_LR, k: float = DEFAULT_K
-) -> Callable:
-    return lr_schedule.exp_decay_fn(epoch_base=epoch_base, lr_initial=lr_initial, k=k)
-
-
-def resnet(
-    input_shape: Tuple[int, int, int] = (32, 32, 3),
-    num_classes: int = 10,
-    version: int = 1,
-    n: int = 3,
-    kernel_initializer: str = KERNEL_INITIALIZER_DEFAULT,
-    l2_factor: float = L2_DEFAULT,
-    l2_dense: Optional[float] = None,
-    dropout: Optional[float] = None,
-):
-    # Computed depth from supplied model parameter n
-    if version == 1:
-        depth = n * 6 + 2
-    elif version == 2:
-        depth = n * 9 + 2
-
-    # Model name, depth and version
-    model_type = "ResNet%dv%d" % (depth, version)
-
-    # Build model
-    if version == 2:
-        model = resnet_v2(
-            input_shape=input_shape,
-            depth=depth,
-            l2_factor=l2_factor,
-            l2_dense=l2_dense,
-            dropout=dropout,
-        )
-    else:
-        model = resnet_v1(
-            input_shape=input_shape,
-            depth=depth,
-            l2_factor=l2_factor,
-            l2_dense=l2_dense,
-            dropout=dropout,
-        )
-    return model, model_type
-
-
-def resnet_layer(
-    inputs,
-    num_filters=16,
-    kernel_size=3,
-    strides=1,
-    activation="relu",
-    batch_normalization=True,
-    conv_first=True,
-    kernel_initializer: str = KERNEL_INITIALIZER_DEFAULT,
-    l2_factor=L2_DEFAULT,
-    dropout: Optional[float] = None,
-):
-    """2D Convolution-Batch Normalization-Activation stack builder.
-
-    Args:
-        inputs (tensor): input tensor from input image or previous layer
-        num_filters (int): Conv2D number of filters
-        kernel_size (int): Conv2D square kernel dimensions
-        strides (int): Conv2D square stride dimensions
-        activation (string): activation name
-        batch_normalization (bool): whether to include batch normalization
-        conv_first (bool): conv-bn-activation (True) or
-            bn-activation-conv (False)
-
-    Returns:
-        x (tensor): tensor as input to the next layer
-    """
-    conv = Conv2D(
-        num_filters,
-        kernel_size=kernel_size,
-        strides=strides,
-        padding="same",
-        kernel_initializer=kernel_initializer,
-        kernel_regularizer=l2(l2_factor),
-    )
-
-    x = inputs
-    if conv_first:
-        x = conv(x)
-        if batch_normalization:
-            x = BatchNormalization()(x)
-        if activation is not None:
-            x = Activation(activation)(x)
-    else:
-        if batch_normalization:
-            x = BatchNormalization()(x)
-        if activation is not None:
-            x = Activation(activation)(x)
-        x = conv(x)
-    if dropout is not None and dropout > 0.0:
-        x = Dropout(dropout)(x)
-    return x
-
-
-def resnet_v1(
-    input_shape,
-    depth,
-    num_classes=10,
-    kernel_initializer: str = KERNEL_INITIALIZER_DEFAULT,
-    l2_factor=L2_DEFAULT,
-    l2_dense: Optional[float] = None,
-    dropout: Optional[float] = None,
-):
-    """ResNet Version 1 Model builder [a]
-
-    Stacks of 2 x (3 x 3) Conv2D-BN-ReLU
-    Last ReLU is after the shortcut connection.
-    At the beginning of each stage, the feature map size is halved (downsampled)
-    by a convolutional layer with strides=2, while the number of filters is
-    doubled. Within each stage, the layers have the same number filters and the
-    same number of filters.
-    Features maps sizes:
-    stage 0: 32x32, 16
-    stage 1: 16x16, 32
-    stage 2:  8x8,  64
-    The Number of parameters is approx the same as Table 6 of [a]:
-    ResNet20 0.27M
-    ResNet32 0.46M
-    ResNet44 0.66M
-    ResNet56 0.85M
-    ResNet110 1.7M
-
-    # Arguments
-        input_shape (tensor): shape of input image tensor
-        depth (int): number of core convolutional layers
-        num_classes (int): number of classes (CIFAR10 has 10)
-
-    # Returns
-        model (Model): Keras model instance
-    """
-    if (depth - 2) % 6 != 0:
-        raise ValueError("depth should be 6n+2 (eg 20, 32, 44 in [a])")
-    # Start model definition.
-    num_filters = 16
-    num_res_blocks = int((depth - 2) / 6)
-
-    inputs = Input(shape=input_shape)
-    x = resnet_layer(inputs=inputs, kernel_initializer=kernel_initializer)
-    # Instantiate the stack of residual units
-    for stack in range(3):
-        for res_block in range(num_res_blocks):
-            strides = 1
-            if stack > 0 and res_block == 0:  # first layer but not first stack
-                strides = 2  # downsample
-            y = resnet_layer(
-                inputs=x,
-                num_filters=num_filters,
-                strides=strides,
-                kernel_initializer=kernel_initializer,
-                l2_factor=l2_factor,
-                dropout=dropout,
-            )
-            y = resnet_layer(
-                inputs=y,
-                num_filters=num_filters,
-                activation=None,
-                kernel_initializer=kernel_initializer,
-                l2_factor=l2_factor,
-                dropout=dropout,
-            )
-            if stack > 0 and res_block == 0:  # first layer but not first stack
-                # linear projection residual shortcut connection to match
-                # changed dims
-                x = resnet_layer(
-                    inputs=x,
-                    num_filters=num_filters,
-                    kernel_size=1,
-                    strides=strides,
-                    activation=None,
-                    batch_normalization=False,
-                    kernel_initializer=kernel_initializer,
-                    l2_factor=l2_factor,
-                    dropout=dropout,
-                )
-            x = keras.layers.add([x, y])
-            x = Activation("relu")(x)
-        num_filters *= 2
-
-    # Add classifier on top.
-    # v1 does not use BN after last shortcut connection-ReLU
-    x = AveragePooling2D(pool_size=8)(x)
-    y = Flatten()(x)
-    outputs = Dense(
-        num_classes,
-        activation="softmax",
-        kernel_initializer="he_normal",
-        kernel_regularizer=l2(l2_dense) if l2_dense is not None else None,
-    )(y)
-
-    # Instantiate model.
-    model = Model(inputs=inputs, outputs=outputs)
-    return model
-
-
-def resnet_v2(
-    input_shape,
-    depth,
-    num_classes=10,
-    kernel_initializer: str = KERNEL_INITIALIZER_DEFAULT,
-    l2_factor=L2_DEFAULT,
-    l2_dense: Optional[float] = None,
-    dropout: Optional[float] = None,
-):
-    """ResNet Version 2 Model builder [b]
-
-    Stacks of (1 x 1)-(3 x 3)-(1 x 1) BN-ReLU-Conv2D or also known as
-    bottleneck layer
-    First shortcut connection per layer is 1 x 1 Conv2D.
-    Second and onwards shortcut connection is identity.
-    At the beginning of each stage, the feature map size is halved (downsampled)
-    by a convolutional layer with strides=2, while the number of filter maps is
-    doubled. Within each stage, the layers have the same number filters and the
-    same filter map sizes.
-    Features maps sizes:
-    conv1  : 32x32,  16
-    stage 0: 32x32,  64
-    stage 1: 16x16, 128
-    stage 2:  8x8,  256
-
-    # Arguments
-        input_shape (tensor): shape of input image tensor
-        depth (int): number of core convolutional layers
-        num_classes (int): number of classes (CIFAR10 has 10)
-
-    # Returns
-        model (Model): Keras model instance
-    """
-    if (depth - 2) % 9 != 0:
-        raise ValueError("depth should be 9n+2 (eg 56 or 110 in [b])")
-    # Start model definition.
-    num_filters_in = 16
-    num_res_blocks = int((depth - 2) / 9)
-
-    inputs = Input(shape=input_shape)
-    # v2 performs Conv2D with BN-ReLU on input before splitting into 2 paths
-    x = resnet_layer(
-        inputs=inputs,
-        num_filters=num_filters_in,
-        conv_first=True,
-        kernel_initializer=kernel_initializer,
-    )
-
-    # Instantiate the stack of residual units
-    for stage in range(3):
-        for res_block in range(num_res_blocks):
-            activation: Optional[str] = "relu"
-            batch_normalization = True
-            strides = 1
-            if stage == 0:
-                num_filters_out = num_filters_in * 4
-                if res_block == 0:  # first layer and first stage
-                    activation = None
-                    batch_normalization = False
-            else:
-                num_filters_out = num_filters_in * 2
-                if res_block == 0:  # first layer but not first stage
-                    strides = 2  # downsample
-
-            # bottleneck residual unit
-            y = resnet_layer(
-                inputs=x,
-                num_filters=num_filters_in,
-                kernel_size=1,
-                strides=strides,
-                activation=activation,
-                batch_normalization=batch_normalization,
-                conv_first=False,
-                kernel_initializer=kernel_initializer,
-                l2_factor=l2_factor,
-                dropout=dropout,
-            )
-            y = resnet_layer(
-                inputs=y,
-                num_filters=num_filters_in,
-                conv_first=False,
-                kernel_initializer=kernel_initializer,
-                l2_factor=l2_factor,
-                dropout=dropout,
-            )
-            y = resnet_layer(
-                inputs=y,
-                num_filters=num_filters_out,
-                kernel_size=1,
-                conv_first=False,
-                kernel_initializer=kernel_initializer,
-                l2_factor=l2_factor,
-                dropout=dropout,
-            )
-            if res_block == 0:
-                # linear projection residual shortcut connection to match
-                # changed dims
-                x = resnet_layer(
-                    inputs=x,
-                    num_filters=num_filters_out,
-                    kernel_size=1,
-                    strides=strides,
-                    activation=None,
-                    batch_normalization=False,
-                    kernel_initializer=kernel_initializer,
-                    l2_factor=l2_factor,
-                    dropout=dropout,
-                )
-            x = keras.layers.add([x, y])
-
-        num_filters_in = num_filters_out
-
-    # Add classifier on top.
-    # v2 has BN-ReLU before Pooling
-    x = BatchNormalization()(x)
-    x = Activation("relu")(x)
-    x = AveragePooling2D(pool_size=8)(x)
-    y = Flatten()(x)
-    outputs = Dense(
-        num_classes,
-        activation="softmax",
-        kernel_initializer=kernel_initializer,
-        kernel_regularizer=l2(l2_dense) if l2_dense is not None else None,
-    )(y)
-
-    # Instantiate model.
-    model = Model(inputs=inputs, outputs=outputs)
-    return model
diff --git a/benchmarks/benchmark/net/resnet_test.py b/benchmarks/benchmark/net/resnet_test.py
deleted file mode 100644
index 31999ea0a..000000000
--- a/benchmarks/benchmark/net/resnet_test.py
+++ /dev/null
@@ -1,13 +0,0 @@
-import pytest
-
-from .resnet import resnet20v2_compiled
-
-
-@pytest.mark.slow
-def test_num_parameters_mnist():
-    # Prepare
-    model = resnet20v2_compiled(input_shape=(32, 32, 3), num_classes=10)
-    # Execute
-    num_params = model.count_params()
-    # Assert
-    assert num_params == 574_090
diff --git a/benchmarks/benchmark/task.py b/benchmarks/benchmark/task.py
deleted file mode 100644
index f4579c11d..000000000
--- a/benchmarks/benchmark/task.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from abc import ABC
-from typing import Optional
-
-DEFAULT_R = 50  # Number of federated learning rounds
-DEFAULT_E = 5  # Number of epochs (on each client, in each round)
-DEFAULT_C = 0.1  # Fraction of participants participating in each round
-DEFAULT_B = 64  # Batch size
-
-DEFAULT_INSTANCE_CORES = 2
-DEFAULT_TIMEOUT = 60
-
-# pylint: disable-msg=too-many-instance-attributes
-class Task(ABC):
-    """DSL primitive used to represent a single benchmark task."""
-
-    def __init__(
-        self,
-        name: str,
-        dataset_name: str,
-        model_name: str,
-        R: int,
-        E: int,
-        C: float,
-        B: int,
-        partition_id: Optional[int] = None,
-        instance_cores: int = DEFAULT_INSTANCE_CORES,
-        timeout: int = DEFAULT_TIMEOUT,
-        label: Optional[str] = None,
-    ):
-        self.name = name
-        self.dataset_name = dataset_name
-        self.model_name = model_name
-        self.R = R
-        self.E = E
-        self.C = C
-        self.B = B
-        self.partition_id = partition_id
-        self.instance_cores = instance_cores
-        self.timeout = timeout
-        self.label = label if label is not None else name
-
-
-class VisionTask(Task):
-    def __init__(
-        self,
-        name: str,
-        dataset_name: str,
-        model_name="blog_cnn",
-        R=DEFAULT_R,
-        E=DEFAULT_E,
-        C=DEFAULT_C,
-        B=DEFAULT_B,
-        instance_cores=DEFAULT_INSTANCE_CORES,
-        timeout: int = DEFAULT_TIMEOUT,
-        label: Optional[str] = None,
-    ):
-        super().__init__(
-            name=name,
-            dataset_name=dataset_name,
-            model_name=model_name,
-            R=R,
-            E=E,
-            C=C,
-            B=B,
-            instance_cores=instance_cores,
-            timeout=timeout,
-            label=label,
-        )
-
-
-class UnitaryVisionTask(Task):
-    def __init__(
-        self,
-        name: str,
-        dataset_name: str,
-        model_name="blog_cnn",
-        E=DEFAULT_R * DEFAULT_E,
-        B=DEFAULT_B,
-        partition_id: int = 0,
-        instance_cores=DEFAULT_INSTANCE_CORES,
-        timeout: int = DEFAULT_TIMEOUT,
-        label: Optional[str] = None,
-    ):
-        super().__init__(
-            name=name,
-            dataset_name=dataset_name,
-            model_name=model_name,
-            R=1,
-            E=E,
-            C=0.0,
-            B=B,
-            partition_id=partition_id,
-            instance_cores=instance_cores,
-            timeout=timeout,
-            label=label,
-        )
diff --git a/benchmarks/conftest.py b/benchmarks/conftest.py
deleted file mode 100644
index fb4cda4d9..000000000
--- a/benchmarks/conftest.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import numpy as np
-import pytest
-from absl import flags
-
-from xain_fl.types import FederatedDataset, KerasDataset
-
-FLAGS = flags.FLAGS
-
-
-def pytest_collection_modifyitems(items):
-    for item in items:
-        if not any(item.iter_markers()):
-            item.add_marker("unmarked")
-
-
-def pytest_runtest_setup():
-    # Invoking FLAGS will make the flags usable for the
-    # test execution and avoid throwing an error
-    FLAGS(
-        argv=[
-            "test",  # some app name required
-            "--fetch_datasets=True",  # resetting to default at beginning of every test
-        ]
-    )
-
-
-@pytest.fixture
-def output_dir(tmpdir):
-    tmpdir = str(tmpdir)
-    FLAGS(["test", f"--output_dir={tmpdir}"])
-
-    return tmpdir
-
-
-@pytest.fixture
-def disable_fetch():
-    FLAGS(["test", "--fetch_datasets=False"])
-
-
-def create_mock_keras_dataset() -> KerasDataset:
-    labels = np.arange(10, dtype=np.int8)
-
-    x_train = np.ones((600, 32, 32, 3), dtype=np.int8)
-    y_train = np.tile(labels, 60)
-    assert x_train.shape[0] == y_train.shape[0]
-
-    x_test = np.ones((100, 32, 32, 3), dtype=np.int8)
-    y_test = np.tile(labels, 10)
-    assert x_test.shape[0] == y_test.shape[0]
-
-    return (x_train, y_train), (x_test, y_test)
-
-
-def create_mock_federated_dataset() -> FederatedDataset:
-    labels = np.arange(10, dtype=np.int8)
-
-    x_train = np.ones((540, 32, 32, 3), dtype=np.int8)
-    y_train = np.tile(labels, 54)
-    assert x_train.shape[0] == y_train.shape[0]
-
-    xy_partitions = list(
-        zip(
-            np.split(x_train, indices_or_sections=2, axis=0),
-            np.split(y_train, indices_or_sections=2, axis=0),
-        )
-    )
-
-    x_val = np.ones((60, 32, 32, 3), dtype=np.int8)
-    y_val = np.tile(labels, 6)
-    assert x_val.shape[0] == y_val.shape[0]
-
-    x_test = np.ones((100, 32, 32, 3), dtype=np.int8)
-    y_test = np.tile(labels, 10)
-    assert x_test.shape[0] == y_test.shape[0]
-
-    return xy_partitions, (x_val, y_val), (x_test, y_test)
-
-
-@pytest.fixture
-def mock_keras_dataset() -> KerasDataset:
-    """dataset mock after it went through internal load method"""
-    return create_mock_keras_dataset()
-
-
-@pytest.fixture
-def mock_federated_dataset() -> FederatedDataset:
-    """dataset mock after it went through internal load method"""
-    return create_mock_federated_dataset()
diff --git a/benchmarks/generator/README.md b/benchmarks/generator/README.md
deleted file mode 100644
index 63559854d..000000000
--- a/benchmarks/generator/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# Generator
-
-Generates various datasets which will than be uploaded into
-All generated datasets will be stored in (by default) ~/.xain-fl/generator/datasets
diff --git a/benchmarks/generator/__init__.py b/benchmarks/generator/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/benchmarks/generator/__main__.py b/benchmarks/generator/__main__.py
deleted file mode 100644
index b7a4a13a2..000000000
--- a/benchmarks/generator/__main__.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from absl import app, logging
-
-from benchmarks.generator import config, data, persistence
-from xain_fl.datasets import testing
-
-
-def generate_dataset(dataset_name):
-    """Creates a dataset and stores it in local generator datasets directory
-
-    Args:
-        dataset_name (str): Name of dataset to be generated. Has to be one of the keys
-            of ~benchmarks.generator.config.datasets
-    """
-    logging.info("Starting dataset generation of {}".format(dataset_name))
-
-    assert dataset_name in config.datasets, "Dataset not found in config"
-
-    c = config.datasets[dataset_name]
-
-    dataset = data.create_federated_dataset(
-        keras_dataset=c["keras_dataset"],
-        num_partitions=c["num_partitions"],
-        validation_set_size=c["validation_set_size"],
-        transformers=c["transformers"],
-        transformers_kwargs=c["transformers_kwargs"],
-    )
-
-    if c["assert_dataset_origin"]:
-        testing.assert_dataset_origin(
-            keras_dataset=data.load(c["keras_dataset"]), federated_dataset=dataset
-        )
-
-    persistence.save_splits(
-        dataset_name=dataset_name,
-        dataset=dataset,
-        local_generator_dir=config.local_generator_datasets_dir,
-    )
-
-
-def main(_):
-    """When called will loop through ~benchmarks.generator.config.datasets
-    and invoke generate_dataset for each dataset
-    """
-    for dataset_name in config.datasets:
-        generate_dataset(dataset_name)
-
-
-app.run(main=main)
diff --git a/benchmarks/generator/class_per_partition_distribution.py b/benchmarks/generator/class_per_partition_distribution.py
deleted file mode 100644
index 172c760fd..000000000
--- a/benchmarks/generator/class_per_partition_distribution.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import numpy as np
-
-# Passed to RandomState for predictable shuffling
-SEED = 851746
-
-
-class Bucket:
-    """Data structure which has an internal storage with unique items which can be
-    picked or exchanged as well as some helper methods to allow reading the current
-    state of the bucket.
-    """
-
-    def __init__(self, num_classes: int, num_per_class: int, dtype=np.int8):
-        """Will initialize a bucket with its own random state as well a storage of numbers as a
-        ndarray with shape `(num_classes)` where each index contains the number `num_per_class`.
-        """
-        self.dtype = dtype
-        self.num_class = num_classes
-
-        # Let the bucket have its own RandomState although beware that each bucket will have it
-        # initialized the same (as long as the default seed is taken). Given a sequence of method
-        # calls the result will always be the same
-        # pylint: disable=no-member
-        self.rst = np.random.RandomState(seed=SEED)
-
-        # index == class and value == how many are left
-        self.storage = np.full((num_classes), num_per_class, dtype=self.dtype)
-
-    def zero_indicies(self):
-        """Returns all indicies which don't have any items left.
-
-        Returns:
-            (ndarray): boolean numpy array of shape (num_classes) with shows which
-                index contains no more items
-        """
-        return np.where(self.storage == 0)[0]
-
-    def multi_indicies(self):
-        """Returns indices which have more than one item left
-
-        Returns:
-            (ndarray): boolean numpy array of shape (num_classes) which shows which
-                index still contain more than 1 item
-        """
-
-        return np.where(self.storage > 1)[0]
-
-    def has_distinct_sections(self, num_distinct_sections: int) -> bool:
-        """Return True if there are at least `num_distinct_sections` distinct items available.
-
-        Args:
-            num_distinct_sections (int): Number of items which should be still available
-        """
-        possible_choices = np.flatnonzero(self.storage)
-        return possible_choices.size >= num_distinct_sections
-
-    def sample(self, num_distinct_sections: int):
-        """Returns `num_distinct_sections` distinct items still available in internal storage.
-
-        Args:
-            num_distinct_sections (int): Number of items to be returned
-        """
-        possible_choices = np.flatnonzero(self.storage)
-        choices = self.rst.choice(
-            possible_choices, num_distinct_sections, replace=False
-        )
-        # pylint: disable=no-member
-        np.subtract.at(self.storage, choices, 1)
-
-        return choices
-
-    def inc_dec(self, index_inc: int, index_dec: int) -> None:
-        # pylint: disable=no-member
-        np.subtract.at(self.storage, [index_dec], 1)
-        np.add.at(self.storage, [index_inc], 1)
-
-
-def distribution(num_classes: int, num_partitions: int, cpp: int) -> np.ndarray:
-    """Returns a unique distribution with `num_classes` and `num_partitions`.
-
-
-    Args:
-        num_classes (int): number of distinct unique classes
-        num_partitions (int): number of partitions
-        cpp (int): number of classes per partition required
-
-    Returns:
-        np.ndarray: parition distribution as an ndarray of shape (num_partitions, num_classes)
-              with ones at the indices where a section should be
-
-    Example:
-        For `distribution(num_classes=4, num_partitions=4, cpp=3)` you will get::
-
-            [
-                [1, 1, 1, 0],
-                [0, 1, 1, 1],
-                [1, 1, 0, 1],
-                [1, 0, 1, 1],
-            ]
-    """
-    # pylint: disable=no-member
-    rst = np.random.RandomState(seed=SEED)
-
-    dtype = np.int8
-    num_sections = cpp * num_partitions
-
-    assert num_sections % num_classes == 0
-    num_per_class = num_sections // num_classes
-
-    partitions = np.zeros((num_partitions, num_classes), dtype=dtype)
-
-    # e.g. [20, 20, 20, 20] for num_classes=4 and cpp = 2
-    bucket = Bucket(num_classes, num_per_class)
-
-    for p in partitions:
-        # 1. Check if there are distinct non-zero values in the bucket
-        while not bucket.has_distinct_sections(cpp):
-            # Swap a section (which is available multiple times) from the bucket
-            # with a section of a partition where that partition does not contain
-            # the bucket section yet
-
-            # pull from partition and fill bucket
-            bucket_zero_indicies = bucket.zero_indicies()
-
-            # pull from bucket and fill partition
-            bucket_multi_indicies = bucket.multi_indicies()
-
-            # sample one index from each
-            bucket_zero_index = rst.choice(bucket_zero_indicies, 1)[0]
-            bucket_multi_index = rst.choice(bucket_multi_indicies, 1)[0]
-
-            # Find partition where:
-            # - index_zero is at least one
-            # - index_multi is zero
-            partition_candidates_zero = np.where(partitions.T[bucket_zero_index] == 1)
-            partition_candidates_multi = np.where(partitions.T[bucket_multi_index] == 0)
-            partition_candidates = np.intersect1d(
-                partition_candidates_zero, partition_candidates_multi
-            )
-
-            # Pick one partiton to modify
-            pc_index = rst.choice(partition_candidates, 1)[0]
-
-            # Swap
-            partitions[pc_index][bucket_multi_index] += 1
-            partitions[pc_index][bucket_zero_index] -= 1
-            bucket.inc_dec(index_inc=bucket_zero_index, index_dec=bucket_multi_index)
-
-        # 2. Sample from the bucket
-        p_indicies = bucket.sample(num_distinct_sections=cpp)
-        np.add.at(p, p_indicies, 1)
-
-    return partitions
diff --git a/benchmarks/generator/class_per_partition_distribution_test.py b/benchmarks/generator/class_per_partition_distribution_test.py
deleted file mode 100644
index ef3db6142..000000000
--- a/benchmarks/generator/class_per_partition_distribution_test.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import numpy as np
-import pytest
-
-from .class_per_partition_distribution import distribution
-
-
-@pytest.mark.parametrize("cpp", [1, 5, 10])
-def test_distribution(cpp):
-    # Prepare
-    num_partitions = 10
-    num_classes = 10
-    num_sections = cpp * num_partitions
-    num_per_class = num_sections / num_classes
-
-    # Execute
-    p_dist = distribution(
-        num_classes=num_classes, num_partitions=num_partitions, cpp=cpp
-    )
-
-    # Assert
-    assert len(p_dist) == num_partitions
-
-    for partition in p_dist:
-        non_zero = np.count_nonzero(partition)
-        assert non_zero == cpp
-
-    assert np.sum(p_dist) == num_sections
-
-    # Each class should occur globally same number of times
-    for column in p_dist.T:
-        assert np.sum(column) == num_per_class
diff --git a/benchmarks/generator/config.py b/benchmarks/generator/config.py
deleted file mode 100644
index 4f0e5372f..000000000
--- a/benchmarks/generator/config.py
+++ /dev/null
@@ -1,141 +0,0 @@
-"""This module contains the federated dataset configs which are used by
-the ~benchmarks.generator.__main__.generate_dataset function
-"""
-import tensorflow as tf
-
-from benchmarks.generator import data
-from benchmarks.generator import partition_volume_distributions as pvd
-from benchmarks.generator import transformer
-from xain_fl import config
-
-local_generator_datasets_dir = config.storage_dir.joinpath("generator/datasets")
-
-keras_cifar10 = tf.keras.datasets.cifar10
-keras_fashion_mnist = tf.keras.datasets.fashion_mnist
-
-# Makes from an int e.g. 5 => 05
-leftpad = lambda i: str(i).zfill(2)
-
-cifar10_cpp = {
-    **{
-        f"cifar-10-100p-noniid-{leftpad(num_cpp)}cpp": {
-            "keras_dataset": keras_cifar10,
-            "transformers": [transformer.class_per_partition],
-            "transformers_kwargs": [{"num_partitions": 100, "cpp": num_cpp}],
-            "num_partitions": 100,
-            "validation_set_size": 5000,
-            "assert_dataset_origin": True,
-        }
-        for num_cpp in [1, 2, 3, 5, 6, 9]
-    },
-    # Edge cases where the default parition volume is not
-    # divisible by given CPP values
-    **{
-        f"cifar-10-100p-noniid-{leftpad(num_cpp)}cpp": {
-            "keras_dataset": keras_cifar10,
-            "transformers": [data.remove_balanced, transformer.class_per_partition],
-            "transformers_kwargs": [
-                {"num_remove": 200},
-                {"num_partitions": 100, "cpp": num_cpp},
-            ],
-            "num_partitions": 100,
-            "validation_set_size": 5000,
-            "assert_dataset_origin": False,
-        }
-        for num_cpp in [4, 7, 8]
-    },
-    "cifar-10-100p-iid-balanced": {
-        "keras_dataset": keras_cifar10,
-        "transformers": [transformer.classes_balanced_randomized_per_partition],
-        "transformers_kwargs": [{"num_partitions": 100}],
-        "num_partitions": 100,
-        "validation_set_size": 5000,
-        "assert_dataset_origin": True,
-    },
-}
-
-fashion_mnist_cpp = {
-    **{
-        f"fashion-mnist-100p-noniid-{leftpad(num_cpp)}cpp": {
-            "keras_dataset": keras_fashion_mnist,
-            "transformers": [transformer.class_per_partition],
-            "transformers_kwargs": [{"num_partitions": 100, "cpp": num_cpp}],
-            "num_partitions": 100,
-            "validation_set_size": 6000,
-            "assert_dataset_origin": True,
-        }
-        # num_cpp 7 and 8 are special cases; see next
-        for num_cpp in [1, 2, 3, 4, 5, 6, 9]
-    },
-    # Edge cases where the default parition volume is not
-    # divisible by given CPP values
-    "fashion-mnist-100p-noniid-07cpp": {
-        "keras_dataset": keras_fashion_mnist,
-        "transformers": [data.remove_balanced, transformer.class_per_partition],
-        "transformers_kwargs": [
-            # we need to remove 100 elements from the full xy_train so the
-            # 540 examples per partition are reduced to 539 and therefore
-            # divisible by 7
-            {"num_remove": 100},
-            {"num_partitions": 100, "cpp": 7},
-        ],
-        "num_partitions": 100,
-        "validation_set_size": 6000,
-        "assert_dataset_origin": False,
-    },
-    "fashion-mnist-100p-noniid-08cpp": {
-        "keras_dataset": keras_fashion_mnist,
-        "transformers": [data.remove_balanced, transformer.class_per_partition],
-        "transformers_kwargs": [
-            # we need to remove 400 elements from the full xy_train so the
-            # 540 examples per partition are reduced to 536 and therefore
-            # divisible by 8
-            {"num_remove": 400},
-            {"num_partitions": 100, "cpp": 8},
-        ],
-        "num_partitions": 100,
-        "validation_set_size": 6000,
-        "assert_dataset_origin": False,
-    },
-    "fashion-mnist-100p-iid-balanced": {
-        "keras_dataset": keras_fashion_mnist,
-        "transformers": [transformer.classes_balanced_randomized_per_partition],
-        "transformers_kwargs": [{"num_partitions": 100}],
-        "num_partitions": 100,
-        "validation_set_size": 6000,
-        "assert_dataset_origin": True,
-    },
-}
-
-
-cifar10_volumes = {
-    f"cifar-10-100p-b{pvd.b_to_str(b)}": {
-        "keras_dataset": keras_cifar10,
-        "transformers": [transformer.random_shuffle],
-        "transformers_kwargs": {},
-        "num_partitions": pvd.dist_to_indicies(dist),
-        "validation_set_size": 5000,
-        "assert_dataset_origin": True,
-    }
-    for b, dist in pvd.cifar_10_100p()
-}
-
-fashion_mnist_volumes = {
-    f"fashion-mnist-100p-b{pvd.b_to_str(b)}": {
-        "keras_dataset": keras_fashion_mnist,
-        "transformers": [transformer.random_shuffle],
-        "transformers_kwargs": {},
-        "num_partitions": pvd.dist_to_indicies(dist),
-        "validation_set_size": 6000,
-        "assert_dataset_origin": True,
-    }
-    for b, dist in pvd.fashion_mnist_100p()
-}
-
-
-datasets = {
-    **cifar10_cpp,
-    **fashion_mnist_cpp,
-    **cifar10_volumes,
-    **fashion_mnist_volumes,
-}
diff --git a/benchmarks/generator/conftest.py b/benchmarks/generator/conftest.py
deleted file mode 100644
index 8a868ae37..000000000
--- a/benchmarks/generator/conftest.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import numpy as np
-import pytest
-
-from benchmarks.conftest import create_mock_keras_dataset
-from xain_fl.types import FederatedDataset, KerasDataset
-
-from . import data
-
-
-class MockKerasDataset:  # pylint: disable=too-few-public-methods
-    """Used as a mock dataset which will go through the load method in the data.py module
-    to make sure that the mock dataset stays compatible with the default load function
-    for all datasets in the project
-    """
-
-    @staticmethod
-    def load_data() -> KerasDataset:
-        return create_mock_keras_dataset()
-
-
-def identitiy_transformer(x: np.ndarray, y: np.ndarray):
-    return x, y
-
-
-@pytest.fixture
-def mock_random_splits_2_dataset() -> FederatedDataset:
-    """Federated dataset mock with two partitions with no transformation"""
-    return data.create_federated_dataset(
-        keras_dataset=MockKerasDataset(),
-        num_partitions=2,
-        validation_set_size=60,
-        transformers=[identitiy_transformer],
-    )
diff --git a/benchmarks/generator/data.py b/benchmarks/generator/data.py
deleted file mode 100644
index de0f2c196..000000000
--- a/benchmarks/generator/data.py
+++ /dev/null
@@ -1,111 +0,0 @@
-from typing import Callable, Dict, List, Optional, Tuple
-
-import numpy as np
-from numpy import ndarray
-
-from xain_fl.types import FederatedDataset, KerasDataset
-
-from .transformer import classes_balanced_randomized_per_partition, sort_by_class
-
-
-def load(keras_dataset) -> KerasDataset:
-    """Loads Keras Dataset in predictable form
-
-    Args:
-        keras_dataset (Dataset)
-
-    Returns:
-        Dataset
-    """
-    (x_train, y_train), (x_test, y_test) = keras_dataset.load_data()
-
-    y_train = y_train.reshape((y_train.shape[0],))
-    y_test = y_test.reshape((y_test.shape[0],))
-
-    return (x_train, y_train), (x_test, y_test)
-
-
-def extract_validation_set(x: ndarray, y: ndarray, size=6000):
-    """Will extract a validation set of "size" from given x,y pair.
-
-    Args:
-        x (ndarray): numpy array
-        y (ndarray): numpy array
-        size (int): Size of validation set. Must be smaller than examples count
-                    in x, y and multiple of label_count
-    """
-    assert x.shape[0] == y.shape[0]
-    assert (
-        x.shape[0] % size == 0
-    ), f"number of examples ({x.shape[0]}) needs to be evenly divisible by parameter size ({size})"
-
-    assert size % len(set(y)) == 0, "size must be a multiple of number of labels"
-
-    x_balanced, y_balanced = classes_balanced_randomized_per_partition(x, y)
-
-    xy_val = (x_balanced[:size], y_balanced[:size])
-    xy_train = (x_balanced[size:], y_balanced[size:])
-
-    return xy_train, xy_val
-
-
-def assert_is_balanced(y):
-    [_, counts] = np.unique(y, return_counts=True)
-    assert len(set(counts)) == 1, "Some classes appear more often than others"
-
-
-def remove_balanced(x: ndarray, y: ndarray, num_remove: int) -> Tuple[ndarray, ndarray]:
-    assert_is_balanced(y)
-
-    num_classes = len(np.unique(y))
-    num_remove_per_class = num_remove // num_classes
-
-    assert (
-        num_remove % num_classes == 0
-    ), "Number of examples to be removed has to be divisible by num_remove"
-
-    x, y = sort_by_class(x, y)
-
-    x_splits = np.split(x, indices_or_sections=num_classes, axis=0)
-    y_splits = np.split(y, indices_or_sections=num_classes, axis=0)
-
-    x = np.concatenate([x_split[num_remove_per_class:] for x_split in x_splits])
-    y = np.concatenate([y_split[num_remove_per_class:] for y_split in y_splits])
-
-    return x, y
-
-
-def create_federated_dataset(
-    keras_dataset,
-    num_partitions: int,
-    validation_set_size: int,
-    transformers: List[Callable],
-    transformers_kwargs: Optional[Dict] = None,
-) -> FederatedDataset:
-    (x_train, y_train), xy_test = load(keras_dataset)
-
-    if isinstance(num_partitions, list):
-        assert x_train.shape[0] % (len(num_partitions) + 1) == 0
-    else:
-        assert x_train.shape[0] % num_partitions == 0
-
-    (x_train, y_train), xy_val = extract_validation_set(
-        x_train, y_train, size=validation_set_size
-    )
-
-    for i, transformer in enumerate(transformers):
-        if (
-            not transformers_kwargs
-            or not transformers_kwargs[i]
-            or transformers_kwargs[i] is None
-        ):
-            x_train, y_train = transformer(x_train, y_train)
-        else:
-            x_train, y_train = transformer(x_train, y_train, **transformers_kwargs[i])
-
-    x_splits = np.split(x_train, indices_or_sections=num_partitions, axis=0)
-    y_splits = np.split(y_train, indices_or_sections=num_partitions, axis=0)
-
-    xy_splits = list(zip(x_splits, y_splits))
-
-    return xy_splits, xy_val, xy_test
diff --git a/benchmarks/generator/data_test.py b/benchmarks/generator/data_test.py
deleted file mode 100644
index a377f2918..000000000
--- a/benchmarks/generator/data_test.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import numpy as np
-import pytest
-import tensorflow as tf
-
-from . import data
-
-
-@pytest.mark.integration
-def test_load():
-    (x_train, y_train), (x_test, y_test) = data.load(tf.keras.datasets.mnist)
-    assert x_train.shape[0] == y_train.shape[0]
-    assert x_test.shape[0] == y_test.shape[0]
-    assert len(x_train.shape) == len(x_test.shape)
-    assert len(y_train.shape) == len(y_test.shape)
-
-
-def test_remove_balanced(mock_keras_dataset):
-    # Prepare
-    (x, y), _ = mock_keras_dataset
-    num_examples = x.shape[0]
-    num_to_be_removed = 100
-    [unique_classes, num_classes_per_class] = np.unique(y, return_counts=True)
-    expected_num_classes_per_class = [
-        n - num_to_be_removed // len(unique_classes) for n in num_classes_per_class
-    ]
-
-    # Execute
-    x, y = data.remove_balanced(x=x, y=y, num_remove=num_to_be_removed)
-
-    # Assert
-    assert isinstance(x, np.ndarray)
-    assert isinstance(y, np.ndarray)
-
-    assert x.shape[0] == y.shape[0] == num_examples - num_to_be_removed
-
-    [_, actual_num_classes_per_class] = np.unique(y, return_counts=True)
-
-    # Each class should occur equal times so we use
-    # the `set` method which makes comparision easier
-    assert set(expected_num_classes_per_class) == set(actual_num_classes_per_class)
-
-
-def test_extract_validation_set():
-    # Prepare
-    example_count = 1000
-    validation_set_size = 100
-    labels = list(range(0, 10))
-    x = np.zeros((example_count, 28, 28), dtype=np.float64)
-    y = np.tile(np.array(labels, dtype=np.int64), example_count // 10)
-
-    # Shuffle to make sure that extract_validation_set
-    # does not expect a sorted array in any form
-    np.random.shuffle(y)
-
-    # Execute
-    (x_train, y_train), (x_val, y_val) = data.extract_validation_set(x, y, size=100)
-
-    # Assert
-    assert (
-        x_val.shape[0] == y_val.shape[0] == validation_set_size
-    ), "Validation set has wrong size"
-    assert (
-        x_train.shape[0] == y_train.shape[0] == example_count - validation_set_size
-    ), "Train set has wrong size"
-
-    assert (
-        set(labels) == set(y_train) == set(y_val)
-    ), "Train and validation set both need to contain all labels"
-
-    label_counts_train = np.unique(y_train, return_counts=True)[1]
-    label_counts_validation = np.unique(y_val, return_counts=True)[1]
-
-    # Each label occurs equal times
-    assert len(set(label_counts_train)) == len(set(label_counts_validation)) == 1
diff --git a/benchmarks/generator/partition_volume_distributions.py b/benchmarks/generator/partition_volume_distributions.py
deleted file mode 100644
index c1c3a4718..000000000
--- a/benchmarks/generator/partition_volume_distributions.py
+++ /dev/null
@@ -1,194 +0,0 @@
-from typing import Dict, List, Tuple
-
-import matplotlib
-import numpy as np
-from absl import app, flags
-
-from benchmarks.helpers import storage
-from xain_fl.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-FLAGS = flags.FLAGS
-
-matplotlib.use("AGG")
-
-# To avoid issues with tkinter we need to set the renderer
-# for matplotlib before importing pyplot
-# As isort would move this line under the "import matplotlib"
-# We need to skip isort explicitly
-# pylint: disable-msg=wrong-import-position, wrong-import-order
-import matplotlib.pyplot as plt  # isort:skip
-
-FORMAT = "png"
-
-bs_fashion_mnist: Dict[float, float] = {
-    1.0: 540.0,
-    1.005: 417.94000000000017,
-    1.01: 317.03099999999995,
-    1.015: 236.25199999999995,
-    1.02: 173.123,
-    1.025: 124.95999999999998,
-    1.03: 89.00600000000003,
-    1.035: 62.66,
-    1.04: 43.67599999999998,
-    1.045: 30.181500000000014,
-}
-"""Parameters b, a (key, value) for ~._exponential_decay for FashionMNIST
-examples distribution. Can be calculated with ~._brute_force_a_for_fashion_mnist
-"""
-
-
-bs_cifar_10: Dict[float, float] = {
-    1.0: 450.0,
-    1.005: 348.351,
-    1.01: 264.28,
-    1.015: 196.89999999999995,
-    1.02: 144.26099999999994,
-    1.025: 104.15400000000004,
-    1.03: 74.18436000000008,
-    1.035: 52.22250000000001,
-    1.04: 36.402,
-    1.045: 25.154300000000013,
-}
-"""Parameters b, a (key, value) for ~._exponential_decay for CIFAR-10
-examples distribution. Can be calculated with ~._brute_force_a_for_cifar_10
-"""
-
-
-def fashion_mnist_100p() -> List[Tuple[float, List[int]]]:
-    """Returns the distribution of examples for a FashionMNIST dataset with 100 partitions
-
-    Returns:
-        List[Tuple[float, List[int]]]: List of tuples with (b, List[int]) where the list of
-            ints describes the number of examples in the partition at the index of the int
-    """
-    return _generate_100p_volume_distributions(bs_fashion_mnist)
-
-
-def cifar_10_100p() -> List[Tuple[float, List[int]]]:
-    """Returns the distribution of examples for a CIFAR-10 dataset with 100 partitions
-
-    Returns:
-        List[Tuple[float, List[int]]]: List of tuples with (b, List[int]) where the list of
-            ints describes the number of examples in the partition at the index of the int
-    """
-    return _generate_100p_volume_distributions(bs_cifar_10)
-
-
-def _exponential_decay(x: int, a: float, b: float) -> float:
-    return a * (b ** x)
-
-
-def _generate_100p_volume_distributions(
-    bs: Dict[float, float]
-) -> List[Tuple[float, List[int]]]:
-    """Returns the distribution of examples for a dataset with 100 partitions
-
-    Args:
-        bs (Dict[float, float]): Dict key will be passed as `b` and value as `a`
-            to ~._exponential_decay function(x, a, b)
-
-    Returns:
-        List[Tuple[float, List[int]]]: List of tuples with (b, List[int]) where the list of
-            ints describes the number of examples in the partition at the index of the int
-    """
-    dists = []
-    for b, a in bs.items():
-        dist = _generate_volume_distribution(np.arange(100), a, b)
-        dists.append((b, dist))
-    return dists
-
-
-def _generate_volume_distribution(xs: np.ndarray, a: float, b: float) -> List[int]:
-    assert xs.ndim == 1
-    return [int(_exponential_decay(x, a=a, b=b)) for x in xs]
-
-
-def _brute_force_a_for_fashion_mnist():
-    for b in [1.0, 1.005, 1.01, 1.015, 1.02, 1.025, 1.03, 1.035, 1.04, 1.045]:
-        a = _brute_force_a(np.arange(100), b, target=54_000)
-        print(f"{b}: {a},")
-
-
-def _brute_force_a_for_cifar_10():
-    for b in [1.0, 1.005, 1.01, 1.015, 1.02, 1.025, 1.03, 1.035, 1.04, 1.045]:
-        a = _brute_force_a(np.arange(100), b, target=45_000)
-        print(f"{b}: {a},")
-
-
-# pylint: disable-msg=inconsistent-return-statements
-def _brute_force_a(xs, b: float, target: int, step=1.0, start=1):
-    a_best = 1
-    for a in np.arange(start, target, step):
-        ys = [int(_exponential_decay(x, a=a, b=b)) for x in xs]
-        sum_ys = sum(ys)
-        if sum_ys == target:
-            return a
-        if sum_ys < target:
-            a_best = a
-        else:
-            # Recursive step
-            return _brute_force_a(xs, b, target, step / 10, start=a_best)
-
-
-def b_to_str(b: float):
-    b_str = f"{b:<f}"
-    return b_str[:5].replace(".", "_")
-
-
-def dist_to_indicies(dist: List[int]) -> List[int]:
-    indices = [0] * len(dist)
-    for i, _ in enumerate(dist):
-        if i == 0:
-            indices[i] = dist[i]
-        else:
-            indices[i] = indices[i - 1] + dist[i]
-
-    assert indices[-1] == sum(dist)
-
-    # Exclude last element as indices only mark start of section
-    return indices[:-1]
-
-
-def _plot_fashion_mnist_dist():
-    dists = fashion_mnist_100p()
-    xs = np.arange(100)
-    plt.figure()
-    legend = []
-    for b, dist in dists:
-        legend.append(str(b))
-        plt.plot(xs, np.array(dist), "o", markersize=1.0)
-    plt.legend(legend, loc="upper left")
-
-    plt.xlabel("Partition ID")
-    plt.ylabel("Examples")
-
-    dname = storage.create_output_subdir("partition_volume_distributions")
-    fname = storage.fname_with_default_dir("plot-part-vol.png", dname)
-
-    plt.savefig(fname=fname, format=FORMAT)
-
-    # FIXME: Matplotlib is currently using agg, which is a non-GUI
-    #        backend, so cannot show the figure.
-    # plt.show()
-
-    return fname
-
-
-def main():
-    """Calculates and prints a, b parameters for calculation of
-    volume distributions
-    """
-    print("Fashion-MNIST:")
-    _brute_force_a_for_fashion_mnist()
-    print("CIFAR-10:")
-    _brute_force_a_for_cifar_10()
-    print("Plot Fashion-MNIST volume distributions")
-    fmd_fpath = _plot_fashion_mnist_dist()
-    logger.info("Data plotted and saved in file", filepath=fmd_fpath)
-
-
-if __name__ == "__main__":
-    app.run(main=lambda _: main())
diff --git a/benchmarks/generator/partition_volume_distributions_test.py b/benchmarks/generator/partition_volume_distributions_test.py
deleted file mode 100644
index 27f9f7959..000000000
--- a/benchmarks/generator/partition_volume_distributions_test.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import numpy as np
-import pytest
-
-from . import partition_volume_distributions as pvd
-
-
-def test_fashion_mnist_distributions():
-    # Prepare
-    expected_sum = 54_000
-
-    # Execute
-    dists = pvd.fashion_mnist_100p()
-
-    # Assert
-    assert len(dists) == 10
-    for dist in dists:
-        assert sum(dist[1]) == expected_sum
-
-
-def test_cifar_10_distributions():
-    # Prepare
-    expected_sum = 45_000
-
-    # Execute
-    dists = pvd.cifar_10_100p()
-
-    # Assert
-    assert len(dists) == 10
-    for dist in dists:
-        assert sum(dist[1]) == expected_sum
-
-
-@pytest.mark.parametrize(
-    "b, expected, target",
-    [
-        (1.0, 540.0, 54_000),
-        (1.005, 417.94000000000017, 54_000),
-        (1.01, 317.03099999999995, 54_000),
-        (1.0, 450.0, 45_000),
-        (1.005, 348.351, 45_000),
-        (1.01, 264.28, 45_000),
-    ],
-)
-def test_brute_force_a(b, expected, target):
-    # Execute
-    # pylint: disable-msg=protected-access
-    actual = pvd._brute_force_a(np.arange(100), b=b, target=target)
-    # Assert
-    assert actual == expected
-
-
-def test_dist_to_indicies():
-    # Prepare
-    dists = pvd.cifar_10_100p() + pvd.fashion_mnist_100p()
-
-    assert len(dists) == 20
-
-    for _, dist in dists:
-        indices = pvd.dist_to_indicies(dist)
-        assert len(indices) == len(dist) - 1
diff --git a/benchmarks/generator/persistence.py b/benchmarks/generator/persistence.py
deleted file mode 100644
index e5598b161..000000000
--- a/benchmarks/generator/persistence.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""
-This module stores and retrives datasets from a given storage_dir
-
-A dataset is stored with N>=0 for N=num_splits-1 as
-- xN.npy
-- yN.npy
-- x_test.npy
-- y_test.npy
-"""
-import os
-from typing import Dict, List, Optional, Tuple
-
-import numpy as np
-from absl import logging
-
-from benchmarks.helpers import storage
-from xain_fl.helpers import sha1
-from xain_fl.types import FederatedDataset, FnameNDArrayTuple
-
-
-def save(fname: str, data: np.ndarray, storage_dir: str):
-    """Stores an ndarray in storage_dir with as fname
-
-    Args:
-        fname (str): Filename
-        data: (np.ndarray): Data to store
-        storage_dir (str): Absolute path to directory in which data should be stored
-
-    Returns:
-        str: SHA1 checksum of stored file
-    """
-    path = "{}/{}".format(storage_dir, fname)
-    np.save(path, data)
-
-    print(f"Saved {path}")
-
-    return sha1.checksum(path)
-
-
-def dataset_to_fname_ndarray_tuple_list(
-    dataset: FederatedDataset
-) -> List[Tuple[str, np.ndarray]]:
-    """Transforms a FederatedDataset into a list of (filename, ndarray) tuples
-    so they can be easily stored on disk.
-
-    Args:
-        dataset (FederatedDataset): The dataset to be transformed
-
-    Returns:
-        List[Tuple[str, np.ndarray]]
-    """
-    fname_ndarray_tuples: List[Tuple[str, np.ndarray]] = []
-    xy_splits, xy_val, xy_test = dataset
-
-    # Add all splits as tuples to fname_ndarray_tuple
-    for i, split in enumerate(xy_splits):
-        fname_ndarray_tuples += to_fname_ndarray_tuple(xy=split, suffix=str(i).zfill(2))
-
-    # Add validation set to files which will be stored
-    fname_ndarray_tuples += to_fname_ndarray_tuple(xy=xy_val, suffix="val")
-
-    # Add test set to files which will be stored
-    fname_ndarray_tuples += to_fname_ndarray_tuple(xy=xy_test, suffix="test")
-
-    return fname_ndarray_tuples
-
-
-def to_fname_ndarray_tuple(
-    suffix: str, xy: Tuple[np.ndarray, np.ndarray]
-) -> List[FnameNDArrayTuple]:
-    """Given a suffix as well as a tuple of ndarrays a list of (filename, ndarray) tuples
-    with length two will be returned.
-
-    Args:
-        suffix (str)
-        xy (Tuple[np.ndarray, np.ndarray])
-
-    Returns:
-        List[FnameNDArrayTuple]
-
-    Example:
-        For `to_fname_ndarray_tuple(suffix="val", xy)` xy beeing a tuple of ndarrays the
-        result will be a list of length two as::
-
-            [
-                ("x_val.npy", xy[0]),
-                ("y_val.npy", xy[1]),
-            ]
-    """
-    x, y = xy
-
-    name_x = f"x_{suffix}.npy"
-    name_y = f"y_{suffix}.npy"
-
-    return [(name_x, x), (name_y, y)]
-
-
-def get_dataset_dir(dataset_name: str, local_generator_dir: str) -> str:
-    """Will return dataset directory and create it if its not already present
-
-    Args:
-        dataset_name (str)
-        local_generator_dir (str): Absolut path
-
-    Returns:
-        str: Absolut path to dataset_dir
-    """
-    dataset_dir = os.path.join(local_generator_dir, dataset_name)
-
-    if not os.path.isdir(dataset_dir):
-        os.makedirs(dataset_dir)
-
-    return dataset_dir
-
-
-def save_splits(dataset_name: str, dataset: FederatedDataset, local_generator_dir: str):
-    """Saves federated dataset to filesystem
-
-    Args:
-        dataset_name (str)
-        dataset (FederatedDataset)
-        local_generator_dir (str)
-    """
-    fname_ndarray_tuple = dataset_to_fname_ndarray_tuple_list(dataset)
-
-    dataset_dir = get_dataset_dir(
-        dataset_name=dataset_name, local_generator_dir=local_generator_dir
-    )
-
-    logging.info("Storing dataset in {}".format(dataset_dir))
-
-    split_hashes: Dict[str, List[Optional[str]]] = {}
-
-    for fname, ndarr in fname_ndarray_tuple:
-        sha1cs = save(fname=fname, data=ndarr, storage_dir=dataset_dir)
-
-        storage_key = fname[2:-4]
-
-        if storage_key not in split_hashes:
-            split_hashes[storage_key] = [None, None]
-
-        split_hashes[storage_key][0 if "x_" in fname else 1] = sha1cs
-
-    hash_file = os.path.join(dataset_dir, f"../../{dataset_name}.json")
-    storage.write_json(split_hashes, hash_file)
-
-    logging.info("{} generated and stored\n".format(dataset_name))
diff --git a/benchmarks/generator/persistence_test.py b/benchmarks/generator/persistence_test.py
deleted file mode 100644
index 197dfa1fc..000000000
--- a/benchmarks/generator/persistence_test.py
+++ /dev/null
@@ -1,184 +0,0 @@
-import os
-
-import numpy as np
-import pytest
-
-from xain_fl.helpers import sha1
-from xain_fl.types import FederatedDataset
-
-from . import persistence
-
-
-# Helper method to compare two federated datasets
-def check_federated_dataset_equality(
-    dataset_expected: FederatedDataset, dataset_actual: FederatedDataset
-):
-    xy_splits_expected, xy_val_expected, xy_test_expected = dataset_expected
-    xy_splits_actual, xy_val_actual, xy_test_actual = dataset_actual
-
-    assert len(xy_splits_expected) == len(xy_splits_actual)
-    assert len(xy_test_expected) == len(xy_test_actual)
-
-    # Assert
-    for xy_expected, xy_actual in zip(xy_splits_expected, xy_splits_actual):
-        x_expected, y_expected = xy_expected
-        x_actual, y_actual = xy_actual
-
-        np.testing.assert_equal(x_expected, x_actual)
-        np.testing.assert_equal(y_expected, y_actual)
-
-    assert xy_val_expected[0].shape == xy_val_actual[0].shape
-    assert xy_val_expected[1].shape == xy_val_actual[1].shape
-
-    assert xy_test_expected[0].shape == xy_test_actual[0].shape
-    assert xy_test_expected[1].shape == xy_test_actual[1].shape
-
-
-def test_dataset_to_fname_ndarray_tuple_list(mock_random_splits_2_dataset):
-    # Prepare
-    fnames_expected = [
-        "x_00.npy",
-        "y_00.npy",
-        "x_01.npy",
-        "y_01.npy",
-        "x_val.npy",
-        "y_val.npy",
-        "x_test.npy",
-        "y_test.npy",
-    ]
-
-    # Execute
-    fname_ndarray_tuples = persistence.dataset_to_fname_ndarray_tuple_list(
-        mock_random_splits_2_dataset
-    )
-
-    # Assert
-    fnames_actual = [n for (n, _) in fname_ndarray_tuples]
-
-    assert set(fnames_actual) == set(fnames_expected)
-
-    for name, arr in fname_ndarray_tuples:
-        assert isinstance(arr, np.ndarray)
-
-        if "test" in name:
-            assert arr.shape[0] == 100
-        elif "val" in name:
-            assert arr.shape[0] == 60
-        else:
-            assert arr.shape[0] == 270
-
-
-def test_to_fname_ndarray_tuple():
-    # Prepare
-    x = np.ones((3, 2))
-    y = np.ones((3))
-
-    t_expected = [("x_00.npy", x), ("y_00.npy", y)]
-
-    # Execute
-    t_actual = persistence.to_fname_ndarray_tuple("00", (x, y))
-
-    # Assert
-    assert t_expected == t_actual
-
-
-@pytest.mark.integration
-def test_save(tmp_path):
-    fname = "xain_test_save_load_single.npy"
-    fpath = os.path.join(tmp_path, fname)
-
-    # Create NumPy array
-    a_expected = np.zeros(shape=(3, 28, 28, 1), dtype=np.uint8)
-    a_expected[0][1][1][0] = 255
-
-    # Execute
-    persistence.save(fname=fname, data=a_expected, storage_dir=tmp_path)
-
-    # Assert
-    a_actual = np.load(fpath)  # load ndarry
-
-    np.testing.assert_equal(a_expected, a_actual)
-
-
-def test_save_splits(monkeypatch, tmp_path, mock_random_splits_2_dataset):
-    # Prepare
-    dataset_name = "mock_dataset"
-    xy_splits, xy_val, xy_test = mock_random_splits_2_dataset
-
-    # -> Files which are supposed to be saved
-    files_to_be_saved = [
-        ("x_00.npy", xy_splits[0][0], tmp_path),
-        ("y_00.npy", xy_splits[0][1], tmp_path),
-        ("x_01.npy", xy_splits[1][0], tmp_path),
-        ("y_01.npy", xy_splits[1][1], tmp_path),
-        ("x_val.npy", xy_val[0], tmp_path),
-        ("y_val.npy", xy_val[1], tmp_path),
-        ("x_test.npy", xy_test[0], tmp_path),
-        ("y_test.npy", xy_test[1], tmp_path),
-    ]
-
-    files_passed_to_save = []
-
-    def mock_save(fname: str, data: np.ndarray, storage_dir: str):
-        files_passed_to_save.append((fname, data, storage_dir))
-
-    def mock_checksum(fpath: str):
-        return "MOCK_CHECKSUM_FOR: {}".format(fpath)
-
-    monkeypatch.setattr(persistence, "save", mock_save)
-    monkeypatch.setattr(sha1, "checksum", mock_checksum)
-
-    # Execute
-    persistence.save_splits(
-        dataset_name=dataset_name,
-        dataset=mock_random_splits_2_dataset,
-        local_generator_dir=tmp_path,
-    )
-
-    dataset_dir = os.path.join(tmp_path, dataset_name)
-
-    # Assert
-    for expected, actual in zip(files_to_be_saved, files_passed_to_save):
-        assert expected[0] == actual[0]
-        assert expected[1].shape == actual[1].shape
-        assert dataset_dir == actual[2]
-
-
-@pytest.mark.integration
-def test_save_load_splits(tmp_path, mock_random_splits_2_dataset):
-    # Prepare
-    dataset_name = "mock_dataset"
-    dataset_dir = os.path.join(tmp_path, dataset_name)
-
-    def fpath(fname):
-        return os.path.join(dataset_dir, fname)
-
-    # Execute
-    # Save splits into tmp directory
-    persistence.save_splits(
-        dataset_name=dataset_name,
-        dataset=mock_random_splits_2_dataset,
-        local_generator_dir=tmp_path,
-    )
-
-    # Assert
-    # Load splits from tmp directory
-    d = {
-        # remove .npy ending with [:-4]
-        fname[:-4]: np.load(fpath(fname))
-        for fname in os.listdir(dataset_dir)
-        if "npy" in fname
-    }
-
-    dataset_actual = (
-        # train set
-        [(d["x_00"], d["y_00"]), (d["x_01"], d["y_01"])],
-        # validation set
-        (d["x_val"], d["y_val"]),
-        # test set
-        (d["x_test"], d["y_test"]),
-    )
-
-    check_federated_dataset_equality(
-        dataset_expected=mock_random_splits_2_dataset, dataset_actual=dataset_actual
-    )
diff --git a/benchmarks/generator/transformer.py b/benchmarks/generator/transformer.py
deleted file mode 100644
index a0e32a372..000000000
--- a/benchmarks/generator/transformer.py
+++ /dev/null
@@ -1,311 +0,0 @@
-from typing import Callable, Tuple
-
-import numpy as np
-from numpy import ndarray
-
-from .class_per_partition_distribution import distribution as cpp_distribution
-
-# Passed to RandomState for predictable shuffling
-SEED = 851746
-
-
-def transfomer_decorator(func: Callable) -> Callable:
-    """The decorator will validate the input and result of any
-    transformer function it is applied to.
-
-    Args:
-        func (Callable): The function to be decorated
-
-    Returns:
-        Callable: Decorated function
-    """
-
-    def wrapper(
-        x: np.ndarray, y: np.ndarray, *args, **kwargs
-    ) -> Tuple[np.ndarray, np.ndarray]:
-        assert x.shape[0] == y.shape[0], "x and y need to have them dimension on axis=0"
-
-        x_transformed, y_transformed = func(x, y, *args, **kwargs)
-
-        assert (
-            x.shape == x_transformed.shape
-        ), "x has to have the same shape after transformation as before"
-        assert (
-            y.shape == y_transformed.shape
-        ), "y has to have the same shape after transformation as before"
-
-        return (x_transformed, y_transformed)
-
-    return wrapper
-
-
-@transfomer_decorator
-def random_shuffle(x: ndarray, y: ndarray) -> Tuple[ndarray, ndarray]:
-    """Will randomly shuffle x and y
-
-    Args:
-        x (ndarray)
-        y (ndarray)
-
-    Returns:
-        Tuple[ndarray, ndarray]
-    """
-    # pylint: disable=no-member
-    permutation = np.random.RandomState(seed=SEED).permutation(x.shape[0])
-    x_shuffled = x[permutation]
-    y_shuffled = y[permutation]
-    return x_shuffled, y_shuffled
-
-
-@transfomer_decorator
-def classes_balanced_randomized_per_partition(
-    x: ndarray, y: ndarray, num_partitions=10
-) -> Tuple[ndarray, ndarray]:
-    """Shuffles dataset so that each partition has the same count of each
-    class equal times.
-
-    Args:
-        x (ndarray)
-        y (ndarray)
-        num_partitions (int): Number of partitions the dataset will later split into
-
-    Returns:
-        Tuple[ndarray, ndarray]
-
-    Example:
-        Assuming three classes with each 30 examples in the full dataset
-        and three paritions we will get::
-
-            [
-                [10, 10, 10]
-                [10, 10, 10]
-                [10, 10, 10]
-            ]
-
-        While each of the 30 examples per class is randomly placed in one
-        of the partitions.
-    """
-    example_count = y.shape[0]
-    section_size = int(example_count / num_partitions)
-
-    assert (
-        example_count % num_partitions == 0
-    ), "Number of examples needs to be evenly divisible by section_count"
-
-    x_shuffled, y_shuffled = random_shuffle(x, y)
-
-    # Array of indices that sort a along the specified axis.
-    sort_index = np.argsort(y_shuffled, axis=0)
-
-    x_sorted = x_shuffled[sort_index]
-    y_sorted = y_shuffled[sort_index]
-
-    balance_index = (
-        np.array(range(example_count), np.int64)
-        .reshape((section_size, num_partitions))
-        .transpose()
-        .reshape(example_count)
-    )
-
-    x_balanced = x_sorted[balance_index]
-    y_balanced = y_sorted[balance_index]
-
-    return x_balanced, y_balanced
-
-
-@transfomer_decorator
-def sort_by_class(x: ndarray, y: ndarray) -> Tuple[ndarray, ndarray]:
-    """Partitions examples by class.
-
-    Args:
-        x (ndarray)
-        y (ndarray)
-
-    Returns:
-        Tuple[ndarray, ndarray]
-    """
-    example_count = y.shape[0]
-    partition_count = np.unique(y).shape[0]
-
-    assert (
-        example_count % partition_count == 0
-    ), "Number of examples needs to be evenly divisible by partition_count"
-
-    # Array of indices that sort a along the specified axis.
-    sort_indexes = np.argsort(y, axis=0)
-
-    x_sorted = x[sort_indexes]
-    y_sorted = y[sort_indexes]
-
-    return x_sorted, y_sorted
-
-
-@transfomer_decorator
-def one_biased_class_per_partition(  # pylint: disable=R0914
-    x: ndarray, y: ndarray, bias=1000
-) -> Tuple[ndarray, ndarray]:
-    """Shuffle y so that the labels are uniformly distributed in each section
-    except one label which will have a bias. Considering the bias the rest
-    needs to be evenly divisible.
-
-
-    Args:
-        x (ndarray)
-        y (ndarray)
-        bias (int): Bias one of the classes in each partition should have
-
-    Returns:
-        Tuple[ndarray, ndarray]
-    """
-    example_count = y.shape[0]
-    # section_count is equal to number of unique labels
-    unique_labels_set = set(y)
-    section_count = len(unique_labels_set)
-    section_size = int(example_count / section_count)
-
-    assert (
-        example_count % section_count == 0
-    ), "Number of examples needs to be evenly divisible by section_count"
-
-    # Array of indices that sort a along the specified axis.
-    sort_indexes = np.argsort(y, axis=0)
-
-    x_sorted = x[sort_indexes]
-    y_sorted = y[sort_indexes]
-
-    x_splits = np.split(x_sorted, indices_or_sections=section_count, axis=0)
-    y_splits = np.split(y_sorted, indices_or_sections=section_count, axis=0)
-
-    # Extract first "bias" from each split
-    x_biased_splits = [x_split[:bias] for x_split in x_splits]
-    y_biased_splits = [y_split[:bias] for y_split in y_splits]
-
-    for y_biased_split in y_biased_splits:
-        # Check that we got single label splits
-        assert len(set(y_biased_split)) == 1
-
-    # Merge rest
-    x_unbiased = np.concatenate([x_split[bias:] for x_split in x_splits])
-    y_unbiased = np.concatenate([y_split[bias:] for y_split in y_splits])
-
-    assert x_unbiased.shape[0] == section_count * (
-        section_size - bias
-    ), "Length of unbiased elements should be equal to original length minus extracted bias"
-
-    # Create balanced shuffle of rest
-    x_balanced, y_balanced = classes_balanced_randomized_per_partition(
-        x_unbiased, y_unbiased, num_partitions=section_count
-    )
-
-    for y_balanced_split in np.split(y_balanced, indices_or_sections=section_count):
-        assert set(y_balanced_split) == unique_labels_set
-
-    # split unbiased splits again to be merged with biased splits
-    x_balanced_splits = np.split(x_balanced, indices_or_sections=section_count, axis=0)
-    y_balanced_splits = np.split(y_balanced, indices_or_sections=section_count, axis=0)
-
-    x_merged = np.concatenate(
-        [
-            np.concatenate([x1, x2], axis=0)
-            for x1, x2 in zip(x_biased_splits, x_balanced_splits)
-        ]
-    )
-    y_merged = np.concatenate(
-        [
-            np.concatenate([y1, y2], axis=0)
-            for y1, y2 in zip(y_biased_splits, y_balanced_splits)
-        ]
-    )
-
-    assert x.shape == x_merged.shape, "Shape of x should not change"
-
-    return x_merged, y_merged
-
-
-@transfomer_decorator
-def class_per_partition(  # pylint: disable=R0914
-    x: ndarray, y: ndarray, num_partitions: int, cpp: int
-) -> Tuple[ndarray, ndarray]:
-    """Transforms dataset so that each partition contains {cpp} classes
-    in a balanced fashion.
-
-    Args:
-        x (ndarray)
-        y (ndarray)
-        num_partitions (int): number of partitions the dataset will have
-        cpp (int): classes per partition
-
-    Returns:
-        Tuple[ndarray, ndarray]
-    """
-    assert x.shape[0] % num_partitions == 0, (
-        f"Number of examples ({x.shape[0]}) needs to be divisible by "
-        + "num_partitions ({num_partitions})"
-    )
-
-    num_classes = len(np.unique(y))
-    num_sections = cpp * num_partitions
-
-    assert num_sections % num_classes == 0, (
-        f"number of sections ({num_sections}) needs to be divisible "
-        + f"by number of classes ({num_classes})"
-    )
-
-    assert x.shape[0] % num_sections == 0, (
-        f"number of examples ({x.shape[0]}) needs to be divisible "
-        + f"by number of sections ({cpp * num_partitions})"
-    )
-
-    section_size = x.shape[0] // num_sections  # number of examples per section
-
-    assert (x.shape[0] / num_classes) % section_size == 0, (
-        f"number of examples per class ({x.shape[0] / num_classes}) needs to be divisible "
-        + f"by number of examples per section ({section_size})"
-    )
-
-    # Array of indices that sort a along the specified axis.
-    sort_indices = np.argsort(y, axis=0)
-
-    # After sorting we will have num_labels sorted sections (e.g. 10 for MNIST)
-    # e.g. with 4 labels and 8 examples (assuming each label occurs equal times)
-    # => y = [0, 0, 1, 1, 2, 2, 3, 3]
-    x_sorted = x[sort_indices]
-    y_sorted = y[sort_indices]
-
-    # We want to achive the following structure
-    # global:      [ class 1 , ..., class N ]
-    # per class:   [ section 1, ..., section N ]
-    # per section: [ example 1, ..., example N ]
-    new_x_shape = (num_classes, num_sections // num_classes, section_size, *x.shape[1:])
-    new_y_shape = (num_classes, num_sections // num_classes, section_size, *y.shape[1:])
-
-    x_sections = x_sorted.reshape(new_x_shape)
-    y_sections = y_sorted.reshape(new_y_shape)
-
-    # Type of dist is List[List[int]] with length num_partitions where each sublist
-    # has length num_class and contains at each index a one if a class section should
-    # occur in the final dataset partition
-    cpp_dist = cpp_distribution(
-        num_classes=num_classes, num_partitions=num_partitions, cpp=cpp
-    )
-
-    _, class_indices = np.nonzero(cpp_dist)
-    section_indices = np.zeros((num_classes), dtype=np.int8)
-
-    x_dist = []
-    y_dist = []
-
-    for c_idx in class_indices:
-        s_idx = section_indices[c_idx]
-        section_indices[c_idx] += 1
-
-        x_sec = x_sections[c_idx][s_idx]
-        y_sec = y_sections[c_idx][s_idx]
-
-        x_dist.append(x_sec)
-        y_dist.append(y_sec)
-
-    x_dist = np.concatenate(x_dist)
-    y_dist = np.concatenate(y_dist)
-
-    return (x_dist, y_dist)
diff --git a/benchmarks/generator/transformer_test.py b/benchmarks/generator/transformer_test.py
deleted file mode 100644
index bb931f7e1..000000000
--- a/benchmarks/generator/transformer_test.py
+++ /dev/null
@@ -1,223 +0,0 @@
-import numpy as np
-import pytest
-
-from . import transformer
-
-
-def test_random_shuffle():
-    # Prepare
-    x = np.array([1, 2, 3, 4])
-    y = np.array([11, 12, 13, 14])
-    # Execute
-    xs, ys = transformer.random_shuffle(x, y)
-    # Assert
-    for x, y in zip(xs, ys):
-        assert x == (y - 10)
-
-
-def test_classes_balanced_randomized_per_partition_wrong_section_count():
-    # Prepare
-    examples = range(100, 200)
-    sorted_labels = range(10)
-
-    x = np.array(examples, dtype=np.int64)
-    y = np.tile(np.array(sorted_labels, dtype=np.int64), 10)
-
-    with pytest.raises(Exception):
-        transformer.classes_balanced_randomized_per_partition(x, y, num_partitions=3)
-
-
-@pytest.mark.parametrize(
-    "section_count, example_count", [(2, 1000), (5, 1000), (10, 1000)]
-)
-def test_classes_balanced_randomized_per_partition(section_count, example_count):
-    # Prepare
-    unique_labels = range(10)  # 10 unique labels
-
-    # Values will at the same time be their original labels
-    # We will later use this for asserting if the label relationship is still present
-    x = np.tile(
-        np.array(unique_labels, dtype=np.int64), example_count // len(unique_labels)
-    )
-
-    # Shuffle to avoid any bias; been there...
-    np.random.shuffle(x)
-
-    y = np.copy(x)
-
-    assert x.shape[0] == y.shape[0]
-
-    # Execute
-    # pylint: disable=line-too-long
-    x_balanced_shuffled, y_balanced_shuffled = transformer.classes_balanced_randomized_per_partition(
-        x, y, num_partitions=section_count
-    )
-
-    # Assert
-    # Create tuples for x,y splits so we can more easily analyze them
-    x_splits = np.split(x_balanced_shuffled, indices_or_sections=section_count, axis=0)
-    y_splits = np.split(y_balanced_shuffled, indices_or_sections=section_count, axis=0)
-
-    # Check that each value still matches its label
-    for (x_split, y_split) in zip(x_splits, y_splits):
-        # Check that the split has the right size
-        assert y_split.shape[0] == int(example_count / section_count)
-        # Check that each segment contains each label
-        assert set(y_split) == set(unique_labels)
-
-        label_count_per_section = example_count / section_count / len(unique_labels)
-
-        for c in np.unique(y_split, return_counts=True)[1]:
-            assert c == label_count_per_section
-
-        for x_i, y_i in zip(x_split, y_split):
-            assert x_i == y_i
-
-
-@pytest.mark.parametrize("bias, example_count", [(10, 1000), (20, 1000), (50, 1000)])
-def test_one_biased_class_per_partition(bias, example_count):  # pylint: disable=R0914
-    # Prepare
-    num_unique_classes = 10
-    unique_labels = range(num_unique_classes)  # 10 unique labels
-    section_size = example_count // num_unique_classes
-    # Amount of labels per partition without any bias
-    unbiased_label_count = (section_size - bias) / num_unique_classes
-
-    x = np.ones((example_count, 28, 28), dtype=np.int64)
-    y = np.tile(np.array(unique_labels, dtype=np.int64), section_size)
-
-    assert x.shape[0] == y.shape[0]
-
-    # Execute
-    x_balanced_shuffled, y_balanced_shuffled = transformer.one_biased_class_per_partition(
-        x, y, bias=bias
-    )
-
-    # Assert
-    # Create tuples for x,y splits so we can more easily analyze them
-    x_splits = np.split(
-        x_balanced_shuffled, indices_or_sections=num_unique_classes, axis=0
-    )
-    y_splits = np.split(
-        y_balanced_shuffled, indices_or_sections=num_unique_classes, axis=0
-    )
-
-    # Check that each value still matches its label
-    for split_index, xy_split in enumerate(zip(x_splits, y_splits)):
-        _, y_split = xy_split
-
-        # Check that the split has the right size
-        assert y_split.shape[0] == int(example_count / num_unique_classes)
-        # Check that each segment contains each label
-        assert set(y_split) == set(
-            unique_labels
-        ), "Each label needs to be present in each section"
-
-        unique_counts = np.unique(y_split, return_counts=True)[1]
-
-        # The first split should contain a bias for the first label
-        # The second split should contain a bias for the second label
-        # repeat untill The "last label should..."
-        for unique_counts_index, unique_count in enumerate(unique_counts):
-            if split_index == unique_counts_index:
-                assert (
-                    unique_count == unbiased_label_count + bias
-                ), "At split_index {} a bias should be present".format(split_index)
-            else:
-                assert unique_count == unbiased_label_count
-
-
-@pytest.mark.parametrize(
-    "example_count, num_partitions, cpp", [(44000, 100, 4), (54000, 100, 6)]
-)
-def test_class_per_partition(
-    cpp, num_partitions, example_count
-):  # pylint: disable=R0914
-    # Prepare
-    num_unique_classes = 10
-    unique_labels = range(num_unique_classes)  # 10 unique labels
-
-    x = np.ones((example_count, 28, 28), dtype=np.int64)
-
-    y = np.tile(
-        np.array(unique_labels, dtype=np.int64), example_count // num_unique_classes
-    )
-
-    # Assert that assumptions about input are correct
-    assert x.shape[0] == example_count
-    assert x.shape[0] == y.shape[0]
-
-    # Execute
-    x_shuffled, y_shuffled = transformer.class_per_partition(
-        x, y, num_partitions=num_partitions, cpp=cpp
-    )
-
-    # Assert
-    assert x.shape == x_shuffled.shape
-    assert y.shape == y_shuffled.shape
-
-    # Create tuples for x,y splits so we can more easily analyze them
-    y_splits = np.split(y_shuffled, indices_or_sections=num_partitions, axis=0)
-
-    actual_cpp = [len(set(y_split)) for y_split in y_splits]
-
-    assert set(actual_cpp) == set([cpp])
-
-
-@pytest.mark.slow
-@pytest.mark.parametrize(
-    "example_count, num_partitions, cpp",
-    [
-        (45000, 100, 1),
-        (45000, 100, 6),
-        (45000, 100, 10),
-        (54000, 100, 1),
-        (54000, 100, 4),
-        (53900, 100, 7),
-        (54000, 100, 10),
-    ],
-)
-def test_class_per_partition_verbose(cpp, num_partitions, example_count):
-    """Purpose of this test is to test even more verbose"""
-    test_class_per_partition(cpp, num_partitions, example_count)
-
-
-@pytest.mark.parametrize(
-    "num_unique_classes, example_count", [(2, 1000), (5, 1000), (10, 1000)]
-)
-def test_sort_by_class(num_unique_classes, example_count):
-    # Prepare
-    unique_labels = range(num_unique_classes)
-
-    # Values will at the same time be their original labels
-    # We will later use this for asserting if the label relationship is still present
-    x = np.tile(
-        np.array(unique_labels, dtype=np.int64), example_count // num_unique_classes
-    )
-
-    # Shuffle to avoid any bias; been there...
-    np.random.shuffle(x)
-
-    y = np.copy(x)
-
-    assert x.shape[0] == y.shape[0]
-
-    # Execute
-    x_sectioned, y_sectioned = transformer.sort_by_class(x, y)
-
-    # Assert
-    # Create tuples for x,y splits so we can more easily analyze them
-    x_splits = np.split(x_sectioned, indices_or_sections=num_unique_classes, axis=0)
-    y_splits = np.split(y_sectioned, indices_or_sections=num_unique_classes, axis=0)
-
-    # Check that each value still matches its label
-    for (x_split, y_split) in zip(x_splits, y_splits):
-        # Check that the split has the right size
-        assert y_split.shape[0] == int(example_count / num_unique_classes)
-
-        # Check that each segment contains only one label
-        assert len(set(y_split)) == 1
-
-        # check that x,y is correctly matched
-        for x_i, y_i in zip(x_split, y_split):
-            assert x_i == y_i
diff --git a/benchmarks/helpers/__init__.py b/benchmarks/helpers/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/benchmarks/helpers/storage.py b/benchmarks/helpers/storage.py
deleted file mode 100644
index 39cdf1acd..000000000
--- a/benchmarks/helpers/storage.py
+++ /dev/null
@@ -1,63 +0,0 @@
-import glob
-import json
-import os
-from typing import Dict
-
-from absl import flags
-
-FLAGS = flags.FLAGS
-
-
-def listdir_recursive(dname: str, relpath=True):
-    """Lists all files found in {dname} with relative path
-
-    Args:
-        dname (str): Absolute path to directory
-
-    Returns:
-        List[str]: List of all files with relative path to dname
-    """
-    files = [
-        fpath
-        for fpath in glob.glob(f"{dname}/**", recursive=True)
-        if os.path.isfile(fpath)
-    ]
-
-    if relpath:
-        return [os.path.relpath(fpath, dname) for fpath in files]
-
-    return files
-
-
-def create_output_subdir(dname: str) -> str:
-    if os.path.isabs(dname):
-        raise Exception("Please provide a relative directory name")
-
-    dname = os.path.join(FLAGS.output_dir, dname)
-
-    os.makedirs(dname, exist_ok=True)
-
-    return dname
-
-
-def fname_with_default_dir(fname: str, dname: str = None) -> str:
-    """Returns fname if its a absolute path otherwise joins it with dname"""
-    if os.path.isabs(fname):
-        return fname
-
-    if dname is None:
-        raise Exception("For relative fname dname is required")
-
-    return os.path.join(dname, fname)
-
-
-def write_json(results: Dict, fname: str):
-    fname = fname_with_default_dir(fname, FLAGS.output_dir)
-    with open(fname, "w") as outfile:
-        json.dump(results, outfile, indent=2, sort_keys=True)
-
-
-def read_json(fname: str):
-    fname = fname_with_default_dir(fname, FLAGS.output_dir)
-    with open(fname, "r") as outfile:
-        return json.loads(outfile.read())
diff --git a/benchmarks/helpers/storage_test.py b/benchmarks/helpers/storage_test.py
deleted file mode 100644
index 01ca0ed3d..000000000
--- a/benchmarks/helpers/storage_test.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-
-from . import storage
-
-
-def test_fname_with_default_dir_absolute_path():
-    # Prepare
-    fname = "/my/absolute/path/myfile"
-    expected_abspath = fname
-
-    # Execute
-    actual_abspath = storage.fname_with_default_dir(fname)
-
-    # Assert
-    assert expected_abspath == actual_abspath
-
-
-def test_fname_with_default_dir_relative_path(output_dir):
-    # Prepare
-    fname = "myfile"
-    expected_abspath = os.path.join(output_dir, fname)
-
-    # Execute
-    actual_abspath = storage.fname_with_default_dir(fname, output_dir)
-
-    # Assert
-    assert expected_abspath == actual_abspath
-    assert output_dir in actual_abspath
diff --git a/benchmarks/ops/__init__.py b/benchmarks/ops/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/benchmarks/ops/__main__.py b/benchmarks/ops/__main__.py
deleted file mode 100644
index 9ad542c65..000000000
--- a/benchmarks/ops/__main__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from absl import app
-
-from . import results
-
-
-def download():
-    app.run(main=lambda _: results.download())
diff --git a/benchmarks/ops/conftest.py b/benchmarks/ops/conftest.py
deleted file mode 100644
index 17dc700a6..000000000
--- a/benchmarks/ops/conftest.py
+++ /dev/null
@@ -1,76 +0,0 @@
-import os
-import random
-import string
-
-import boto3
-import pytest
-from absl import flags
-
-FLAGS = flags.FLAGS
-
-
-client = boto3.client("s3")
-
-
-def random_string(length: int):
-    return "".join(random.sample(string.ascii_lowercase, length))
-
-
-@pytest.fixture
-def populated_output_dir(tmpdir):
-    """Creates an output_dir with 10 files in it"""
-    tmpdir = str(tmpdir)
-    FLAGS(["test", f"--output_dir={tmpdir}"])
-
-    for i in range(10):
-        fname = os.path.join(tmpdir, f"file_{i}.txt")
-
-        # create a new file and open it for writing
-        with open(fname, "x") as f:
-            f.write("To write or not to write?")
-            f.close()
-
-    return tmpdir
-
-
-@pytest.fixture
-def populated_results_dir(tmpdir):
-    """Create a results_dir containing one file in a subdirectory"""
-    tmpdir = str(tmpdir)
-    FLAGS(["test", f"--results_dir={tmpdir}"])
-
-    # Create a subdirectory to make the case more complex
-    dname = os.path.join(tmpdir, "some_dir")
-    os.mkdir(dname)
-
-    fname = os.path.join(dname, "some_file.txt")
-
-    # create a new file and open it for writing
-    with open(fname, "x") as f:
-        f.write("To write or not to write?")
-        f.close()
-
-    return tmpdir
-
-
-@pytest.yield_fixture
-def populated_S3_bucket():
-    bucket = FLAGS.S3_results_bucket
-
-    rnd_str = random_string(5)
-    files = ["file_1.txt", "file_2.txt"]
-
-    s3_keys = []
-
-    for fname in files:
-        key = "integration_test/populated_S3_bucket_fixture_" + f"{rnd_str}/{fname}"
-        s3_keys.append(key)
-
-        client.put_object(Bucket=bucket, Key=key, Body=b"foobar")
-
-    yield s3_keys
-
-    # Clean up after test
-    client.delete_objects(
-        Bucket=bucket, Delete={"Objects": [{"Key": key} for key in s3_keys]}
-    )
diff --git a/benchmarks/ops/docker.py b/benchmarks/ops/docker.py
deleted file mode 100644
index 84c827f49..000000000
--- a/benchmarks/ops/docker.py
+++ /dev/null
@@ -1,84 +0,0 @@
-import subprocess
-from time import strftime
-
-from absl import app
-from faker import Faker
-from faker.providers import person
-
-from xain_fl.helpers import project
-
-fake = Faker()
-fake.add_provider(person)
-
-root_dir = project.root()
-
-
-def get_image_name(tag: str):
-    """Returns docker image name by joining repo with tag
-
-    Args:
-        tag (str): docker image tag to be used
-    """
-    ECR_REPO = "693828385217.dkr.ecr.eu-central-1.amazonaws.com/xain"
-    return f"{ECR_REPO}:{tag}"
-
-
-def generate_unique_tag():
-    """Return a unique string with utc time and human readable part
-
-    Returns:
-        str
-    """
-
-    utc_time = strftime("%Y%m%dT%H%M")
-    # pylint: disable=no-member
-    fake_name = fake.name().lower().replace(" ", "_").replace(".", "")
-
-    return f"{utc_time}_{fake_name}"
-
-
-def build(should_push: bool = False):
-    """Build xain docker container and tag it uniquely
-    If image already exists a new tag will be added anyway
-
-    Args:
-        tag (str): docker image tag to be used
-    """
-    tag = generate_unique_tag()
-    image_name_unique = get_image_name(tag)
-
-    command = ["docker", "build", ".", "-t", image_name_unique]
-    subprocess.run(command, cwd=root_dir).check_returncode()
-
-    if should_push:
-        push(tag)
-
-    return image_name_unique
-
-
-def push(tag: str):
-    """Push xain docker container
-
-    Args:
-        tag (str): docker image tag to be used
-    """
-    # User to get the docker login command with the AWS SDK
-    get_login_command = ["aws", "ecr", "get-login", "--no-include-email"]
-    push_command = ["docker", "push", get_image_name(tag)]
-
-    # Get docker login command and decode into utf-8. Afterwards split
-    # to pass it further to the next subprocess call
-    docker_login_command = (
-        subprocess.check_output(get_login_command, cwd=root_dir)
-        .decode("utf-8")
-        .strip()
-        .split(" ")
-    )
-
-    subprocess.run(docker_login_command, cwd=root_dir).check_returncode()
-    subprocess.run(push_command, cwd=root_dir).check_returncode()
-
-
-# For convenience allow calling module to build and push latest image
-if __name__ == "__main__":
-    app.run(main=lambda _: build(should_push=True))
diff --git a/benchmarks/ops/ec2.py b/benchmarks/ops/ec2.py
deleted file mode 100644
index cb786e6fe..000000000
--- a/benchmarks/ops/ec2.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import re
-
-
-def user_data(image: str, timeout: int, S3_results_bucket: str, flags: str):
-    """Generates EC2 instance user data script which is basically
-    a bash script which will be executed as soon as the machine is
-    up and running
-
-    Args:
-        image (str): docker image as `repository:tag`
-        timeout (int): timeout in minutes
-        flags (str): Will be appended to docker run command e.g. for `--foo=bar --baz=boo`
-                    it will result in `docker run IMAGE --foo=bar --baz=boo`
-
-    Returns:
-        user_data (str): user_data script for EC2 instance returned as in ascii encoding
-    """
-    data = [
-        "#!/bin/bash",
-        "set -x",
-        # Set automatic shutdown after 5 hours
-        f"sudo shutdown -P {timeout}",
-        # Login into ECR. This only works because we assigned
-        # the right IAM instance profile to the EC2 instance
-        "$(aws ecr get-login --region eu-central-1 --no-include-email)",
-        # Pull docker job
-        f"docker pull {image}",
-        # Start docker job
-        "mkdir -p /opt/app/output",
-        "cd /opt/app/",
-        f"docker run \
-            -e S3_RESULTS_BUCKET={S3_results_bucket} \
-            -v $(pwd)/output:/opt/app/output \
-            {image} python -m benchmarks.benchmark.exec {flags} >& $(pwd)/output/training.log",
-        # Cancel previous shutdown and shutdown 1m after the job finishes
-        # The machine is setup to terminate on shutdown
-        "shutdown -c",
-        "shutdown -P 1",
-    ]
-
-    # Replace multiple whitespaces with single whitespaces to allow
-    # the use of line breaks in data to increase readability while
-    # keeping the final output also readable
-    data = [re.sub(r"\s+", " ", s) for s in data]
-
-    return "\n".join(data)
diff --git a/benchmarks/ops/results.py b/benchmarks/ops/results.py
deleted file mode 100755
index 34a95eacb..000000000
--- a/benchmarks/ops/results.py
+++ /dev/null
@@ -1,91 +0,0 @@
-import os
-
-import boto3
-from absl import flags
-
-from benchmarks.helpers.storage import listdir_recursive
-from xain_fl.logger import get_logger
-
-FLAGS = flags.FLAGS
-
-client = boto3.client("s3")
-
-
-logger = get_logger(__name__)
-
-
-def push(group_name: str, task_name: str, output_dir: str = None):
-    """Push everything in output directory to the S3_results_bucket on AWS S3
-
-    Args:
-        group_name (str)
-        task_name (str)
-        output_dir (str): If None FLAGS.output_dir will be used
-    """
-    # If no output_dir was explicitly set use output_dir which was passed via absl flags
-    # and defaults to "git_root/output" and upload everything in it as results
-    if output_dir is None:
-        output_dir = FLAGS.output_dir
-
-    bucket = FLAGS.S3_results_bucket
-
-    ignored_files = [".gitkeep", ".DS_Store"]
-    files = listdir_recursive(output_dir)
-
-    for fname in files:
-        if fname in ignored_files:
-            continue
-
-        local_path = os.path.join(output_dir, fname)
-        key = os.path.join(group_name, task_name, fname)
-
-        # pylint: disable=bare-except
-        try:
-            # Will throw an error if object does not exist
-            client.head_object(Bucket=bucket, Key=key)
-            logger.info("Key is already uploaded", key=key)
-        # TODO: fix this by using something similar to:
-        # https://stackoverflow.com/questions/33842944/check-if-a-key-exists-in-a-bucket-in-s3-using-boto3
-        except:
-            logger.info(
-                "Uploading file to bucket",
-                local_path=local_path,
-                bucket=bucket,
-                key=key,
-            )
-            client.upload_file(local_path, bucket, key)
-
-
-def download():
-    """Download all remote results which are locally not present from the S3_BUCKET on AWS S3"""
-    # Use results_dir which was passed via absl flags and defaults to
-    # "git_root/results" and upload everything in it as results
-    results_dir = FLAGS.results_dir
-    bucket = FLAGS.S3_results_bucket
-
-    # Get list of all files which where uploaded to the bucket which contain
-    # the group_name => integration_test
-    all_objs = client.list_objects_v2(Bucket=bucket)
-
-    if "Contents" not in all_objs:
-        logger.info("No results to download")
-        return
-
-    actual_objs = [obj["Key"] for obj in all_objs["Contents"]]
-
-    already_downloaded_files = listdir_recursive(results_dir)
-
-    # enumerate local files recursively
-    for key in actual_objs:
-        if key not in already_downloaded_files:
-            fname = os.path.join(results_dir, key)
-            dname = os.path.dirname(fname)
-
-            # Instead of checking just create with exist_ok
-            os.makedirs(dname, exist_ok=True)
-
-            logger.info(
-                "Downloading file from bucket", filename=fname, bucket=bucket, key=key
-            )
-
-            client.download_file(Bucket=bucket, Key=key, Filename=fname)
diff --git a/benchmarks/ops/results_test.py b/benchmarks/ops/results_test.py
deleted file mode 100644
index 37f16b073..000000000
--- a/benchmarks/ops/results_test.py
+++ /dev/null
@@ -1,74 +0,0 @@
-import os
-import random
-import string
-
-import boto3
-import pytest
-from absl import flags
-
-from . import results
-
-FLAGS = flags.FLAGS
-
-
-client = boto3.client("s3")
-
-
-@pytest.mark.slow
-@pytest.mark.integration
-def test_push_results(populated_output_dir):
-    # Prepare
-    bucket = FLAGS.S3_results_bucket
-    group_name = "integration_test"
-    task_name = "".join(random.sample(string.ascii_lowercase, 5))
-    # List all files which where in output directory
-    expected_objs = [
-        os.path.join(group_name, task_name, fname)
-        for fname in os.listdir(populated_output_dir)
-    ]
-
-    # Execute
-    results.push(group_name=group_name, task_name=task_name)
-
-    # Assert
-    # Get list of all files which where uploaded to the bucket which contain
-    # the group_name => integration_test
-    all_objs = client.list_objects_v2(Bucket=bucket)
-    actual_objs = [obj["Key"] for obj in all_objs["Contents"]]
-
-    # Check if all files where uploaded
-    assert set(expected_objs).issubset(set(actual_objs)), "Could not upload all files"
-
-    # Cleanup
-    response = client.delete_objects(
-        Bucket=bucket, Delete={"Objects": [{"Key": key} for key in expected_objs]}
-    )
-
-    assert (
-        response["ResponseMetadata"]["HTTPStatusCode"] == 200
-    ), "Cleaning up the bucket failed"
-
-
-@pytest.mark.slow
-@pytest.mark.integration
-def test_download_results(populated_results_dir, populated_S3_bucket):
-    # Prepare
-    expected_files = populated_S3_bucket
-
-    # Execute
-    results.download()
-
-    actual_files = []
-
-    # Assert
-    for root, _, files in os.walk(populated_results_dir):
-        for filename in files:
-            local_path = os.path.join(root, filename)
-            # get relative path to results_dir
-            relative_path = os.path.relpath(local_path, populated_results_dir)
-            actual_files.append(relative_path)
-
-    # As we might have files in our results directory which are not on S3
-    # anymore we will only check if everything which is on S3 was actualy downloaded
-    assert len(actual_files) >= len(expected_files)
-    assert set(expected_files).issubset(set(actual_files))
diff --git a/benchmarks/ops/run.py b/benchmarks/ops/run.py
deleted file mode 100644
index fc1614659..000000000
--- a/benchmarks/ops/run.py
+++ /dev/null
@@ -1,144 +0,0 @@
-import os
-import subprocess
-from typing import Dict
-
-import boto3
-from absl import flags
-
-from xain_fl.helpers import project
-from xain_fl.logger import get_logger
-
-from .ec2 import user_data
-
-logger = get_logger(__name__)
-
-
-FLAGS = flags.FLAGS
-root_dir = project.root()
-
-# Note:
-# We actually would like to use the m5.large up to m5.24xlarge
-# but AWS is not easily willing to give us the increase without
-# asking again and again and again for limit increases.
-# Therefore we switches to using c4 which have higher default limits
-cores: Dict[int, str] = {
-    2: "c4.large",
-    4: "c4.xlarge",
-    8: "c4.2xlarge",
-    16: "c4.4xlarge",
-    32: "c4.8xlarge",
-}
-
-
-def docker(image: str, timeout: int = 300, instance_cores=2, **kwargs):
-    """Run train in docker while accepting an arbitrary
-    number of absl flags to be passed to the docker container
-
-    Args:
-        image (str): docker image name
-        timeout (int): timeout in minutes
-        instance_cores (int): number of cpu cores to be used, if num is to high os.cpu_count()
-                              will be used
-        **kwargs: Will be turned into "--{arg}={kwargs[arg]" format and
-                  passed to docker container
-    """
-    instance_cores = (
-        instance_cores if instance_cores <= os.cpu_count() else os.cpu_count()
-    )
-
-    command = [
-        "docker",
-        "run",
-        "-d",
-        f"--stop-timeout={timeout}",
-        f"--cpus={instance_cores}",
-        "-e",
-        "AWS_ACCESS_KEY_ID",
-        "-e",
-        "AWS_SECRET_ACCESS_KEY",
-        "-e",
-        f"S3_RESULTS_BUCKET={FLAGS.S3_results_bucket}",
-        image,
-        "python",
-        "-m",
-        "benchmarks.benchmark.exec",
-    ]
-
-    for arg in kwargs:
-        if kwargs[arg] is None:
-            # Don't pass flags where arg has value None
-            continue
-        command.append(f"--{arg}={kwargs[arg]}")
-
-    subprocess.run(command, cwd=root_dir)
-
-
-def ec2(image: str, timeout: int = 300, instance_cores=2, **kwargs):
-    """Runs job on EC2 instead of a local machine
-
-    Possible options for instance_type (CPU only) are:
-    - m5.large:     2 vCPU,   8 GB RAM
-    - m5.xlarge:    4 vCPU,  16 GB RAM
-    - m5.2xlarge:   8 vCPU,  32 GB RAM
-    - m5.4xlarge:  16 vCPU,  64 GB RAM
-    - m5.8xlarge:  32 vCPU, 128 GB RAM
-    - m5.12xlarge: 48 vCPU, 192 GB RAM
-    - m5.16xlarge: 64 vCPU, 256 GB RAM
-    - m5.24xlarge: 96 vCPU, 384 GB RAM
-
-    Args:
-        image (str): docker image name
-        timeout (int): timeout in minutes
-        instance_cores (int): number of EC2 instance cpu cores
-        **kwargs: Will be turned into "--{arg}={kwargs[arg]" format and passed to docker container
-    """
-    assert (
-        instance_cores in cores
-    ), f"instance_cores {instance_cores} not in {cores.keys()}"
-    instance_type = cores[instance_cores]
-
-    absl_flags = ""  # Will be passed to docker run in EC2 instance
-
-    for arg in kwargs:
-        if kwargs[arg] is None:
-            # Don't pass flags where arg has value None
-            continue
-        absl_flags += f"--{arg}={kwargs[arg]} "
-
-    absl_flags = absl_flags.strip()
-
-    instance_name = (
-        f"{kwargs['group_name']}_{kwargs['task_name']}"
-    )  # Will be used to make the instance easier identifyable
-
-    udata = user_data(
-        image=image,
-        timeout=timeout,
-        S3_results_bucket=FLAGS.S3_results_bucket,
-        flags=absl_flags,
-    )
-
-    client = boto3.client("ec2")
-    run_response = client.run_instances(
-        ImageId="ami-08806c999be9493f1",
-        MinCount=1,
-        MaxCount=1,
-        InstanceType=instance_type,
-        KeyName="xain-ec2-remote-training",
-        SubnetId="subnet-1bc3c466",
-        IamInstanceProfile={"Name": "XainEC2RemoteTraining"},
-        SecurityGroupIds=["sg-01ff10b690dffbaf5", "sg-01207b671ffadadf5"],
-        InstanceInitiatedShutdownBehavior="terminate",
-        UserData=udata,
-        TagSpecifications=[
-            {
-                "ResourceType": "instance",
-                "Tags": [{"Key": "Name", "Value": instance_name}],
-            }
-        ],
-        AdditionalInfo=absl_flags,  # Helpful to identify instance in EC2 UI
-    )
-    instance_id = run_response["Instances"][0]["InstanceId"]
-    logger.info(
-        "Started EC2 instance", instance_id=instance_id, instance_name=instance_name
-    )
diff --git a/benchmarks/train_remote.py b/benchmarks/train_remote.py
deleted file mode 100644
index 716c29106..000000000
--- a/benchmarks/train_remote.py
+++ /dev/null
@@ -1,23 +0,0 @@
-"""Used as entry_point for console_script `train_remote` in `setup.py`.
-
-Expects the following flags:
-
-    - benchmark_name
-
-Example:
-    train_remote --benchmark_name=flul-fashion-mnist-100p-iid-balanced --group_name=GROUP_NAME
-"""
-from absl import app, flags
-
-from benchmarks.benchmark import benchmark
-
-FLAGS = flags.FLAGS
-
-
-def main():
-    flags.mark_flag_as_required("benchmark_name")
-    app.run(main=benchmark.main)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/conftest.py b/conftest.py
deleted file mode 100644
index a350b319d..000000000
--- a/conftest.py
+++ /dev/null
@@ -1,26 +0,0 @@
-"""
-This conftest should only contain options for pytest
-itself but nothing like fixtures etc.
-"""
-import pytest
-
-
-def pytest_addoption(parser):
-    parser.addoption(
-        "--runslow", action="store_true", default=False, help="run slow tests"
-    )
-
-
-def pytest_configure(config):
-    config.addinivalue_line("markers", "slow: mark test as slow to run")
-
-
-def pytest_collection_modifyitems(config, items):
-    skip_slow = pytest.mark.skip(reason="need --runslow option to run")
-
-    for item in items:
-        if not any(item.iter_markers()):
-            item.add_marker("unmarked")
-
-        if not config.getoption("--runslow") and "slow" in item.keywords:
-            item.add_marker(skip_slow)
diff --git a/docker-compose-dev.yml b/docker-compose-dev.yml
new file mode 100644
index 000000000..c17628314
--- /dev/null
+++ b/docker-compose-dev.yml
@@ -0,0 +1,74 @@
+version: "3.7"
+services:
+
+  minio-dev:
+    image: minio/minio # Stable minio
+    volumes:
+      - minio-data:/data
+    networks:
+      - xain-fl-dev
+    environment:
+      MINIO_ACCESS_KEY: minio
+      MINIO_SECRET_KEY: minio123
+    command:
+        server /data
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
+      interval: 30s
+      timeout: 20s
+      retries: 3
+    ports:
+      - "9000:9000"
+
+  initial-buckets:
+    image: minio/mc
+    depends_on:
+      - minio-dev
+    environment:
+      MINIO_ACCESS_KEY: minio
+      MINIO_SECRET_KEY: minio123
+    networks:
+      - xain-fl-dev
+    entrypoint: >
+      /bin/sh -c "
+        echo Waiting for minio service to start...;
+        while ! nc -z minio-dev 9000;
+        do
+          sleep 5;
+        done;
+        echo Connected!;
+        mc config host add dev-minio http://minio-dev:9000 $${MINIO_ACCESS_KEY} $${MINIO_SECRET_KEY};
+        /usr/bin/mc mb -p dev-minio/xain-fl-temporary-weights;
+        /usr/bin/mc mb -p dev-minio/xain-fl-aggregated-weights;
+        /usr/bin/mc policy set upload dev-minio/xain-fl-temporary-weights;
+        /usr/bin/mc policy set download dev-minio/xain-fl-temporary-weights;
+        /usr/bin/mc policy set upload dev-minio/xain-fl-aggregated-weights;
+        /usr/bin/mc policy set download dev-minio/xain-fl-aggregated-weights;
+        /usr/bin/mc admin trace -v -e dev-minio;
+      "
+
+  xain-fl-dev:
+    environment:
+      MINIO_ACCESS_KEY: minio
+      MINIO_SECRET_KEY: minio123
+    build:
+      context: .
+      dockerfile: Dockerfile.dev
+    command: sh -c "coordinator -f test_array.npy --storage-endpoint http://minio-dev:9000 --storage-key-id $${MINIO_ACCESS_KEY} --storage-secret-access-key $${MINIO_SECRET_KEY} --storage-bucket xain-fl-aggregated-weights"
+    volumes:
+      #  don't use the local egg-info, if one exists
+      - /app/xain_fl.egg-info
+      - ./xain_fl:/app/xain_fl
+      - ${PWD}/setup.py:/app/setup.py
+      - ${PWD}/README.md:/app/README.md
+      - ${PWD}/test_array.npy:/app/test_array.npy
+    networks:
+      - xain-fl-dev
+    ports:
+    - "50051:50051"
+
+volumes:
+  minio-data:
+
+networks:
+  xain-fl-dev:
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 000000000..d5cb8120f
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,35 @@
+version: "3.7"
+services:
+
+  minio:
+    image: minio/minio # Stable minio
+    volumes:
+      - minio-data:/data
+    networks:
+      - xain-fl
+    environment:
+      MINIO_ACCESS_KEY: minio
+      MINIO_SECRET_KEY: minio123
+    command: server /data
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:9000/minio/health/live"]
+      interval: 30s
+      timeout: 20s
+      retries: 3
+    expose:
+      - "9000"
+
+  xain-fl:
+    build:
+      context: .
+      dockerfile: Dockerfile
+    networks:
+      - xain-fl
+    ports:
+    - "50051:50051"
+
+volumes:
+  minio-data:
+
+networks:
+  xain-fl:
diff --git a/docker/entrypoint.sh b/docker/entrypoint.sh
new file mode 100755
index 000000000..a38854506
--- /dev/null
+++ b/docker/entrypoint.sh
@@ -0,0 +1,12 @@
+#!/usr/bin/env sh
+
+set -o errexit
+set -o pipefail
+set -o nounset
+# set -o xtrace
+
+if [ $# -eq 0 ]; then
+    exec coordinator -f test_array.npy --host ${HOST} --port ${PORT}
+else
+    exec coordinator "$@"
+fi
diff --git a/docs/Makefile b/docs/Makefile
index e8e7ce2eb..66bf9efbb 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -23,5 +23,6 @@ show:
 	open _build/html/index.html
 
 docs:
+	rm -rf _code_reference_*
 	make clean
 	make html
diff --git a/docs/README.md b/docs/README.md
new file mode 120000
index 000000000..32d46ee88
--- /dev/null
+++ b/docs/README.md
@@ -0,0 +1 @@
+../README.md
\ No newline at end of file
diff --git a/docs/_static/sequence2.png b/docs/_static/sequence2.png
index 60b968822..ccc981cef 100644
Binary files a/docs/_static/sequence2.png and b/docs/_static/sequence2.png differ
diff --git a/docs/conf.py b/docs/conf.py
index 3372eff44..b54339c68 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -9,10 +9,12 @@
 # If extensions (or modules to document with autodoc) are in another directory,
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
-#
+
 import os
 import sys
 
+from sphinx.ext import apidoc
+
 sys.path.insert(0, os.path.abspath(".."))
 
 
@@ -41,12 +43,14 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    "recommonmark",
+    "m2r",
     "sphinx.ext.napoleon",
     "sphinx.ext.autodoc",
     "sphinx.ext.intersphinx",
     "sphinx.ext.mathjax",
     "sphinxcontrib.mermaid",
+    "sphinx.ext.todo",
+    "sphinx_autodoc_typehints",
 ]
 
 source_suffix = {".rst": "restructuredtext", ".md": "markdown"}
@@ -64,7 +68,7 @@
 
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
-#
+
 html_theme = "alabaster"
 
 # https://alabaster.readthedocs.io/en/latest/customization.html
@@ -87,13 +91,12 @@
 intersphinx_mapping = {
     "python": ("https://docs.python.org/3", None),
     "numpy": ("https://docs.scipy.org/doc/numpy/", None),
+    "grpc": ("https://grpc.github.io/grpc/python/", None),
 }
 
 
 def run_apidoc(_):
-    from sphinx.ext import apidoc
-
-    exclude = ["../xain_fl/**_test.py"]
+    exclude = []
 
     argv = [
         "--doc-project",
@@ -111,23 +114,25 @@ def run_apidoc(_):
 
     apidoc.main(argv)
 
-    exclude_benchmark = ["../benchmarks/**_test.py"]
-
-    argv_benchmark = [
-        "--doc-project",
-        "Code Reference Benchmarks",
-        "-M",
-        "-f",
-        "-d",
-        "3",
-        "--tocfile",
-        "index",
-        "-o",
-        "./_code_reference_benchmarks/",
-        "../benchmarks/",
-    ] + exclude_benchmark
-
-    apidoc.main(argv_benchmark)
+    # TODO: (XP-350): Decide about benchmark documentation after moving benchmarks to
+    # separate repository
+    # exclude_benchmark = ["../benchmarks/**_test.py"]
+
+    # argv_benchmark = [
+    #     "--doc-project",
+    #     "Code Reference Benchmarks",
+    #     "-M",
+    #     "-f",
+    #     "-d",
+    #     "3",
+    #     "--tocfile",
+    #     "index",
+    #     "-o",
+    #     "./_code_reference_benchmarks/",
+    #     "../benchmarks/",
+    # ] + exclude_benchmark
+
+    # apidoc.main(argv_benchmark)
 
 
 def setup(app):
diff --git a/docs/index.rst b/docs/index.rst
index c7786de79..85233293f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -3,51 +3,12 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-XAIN: Federated Learning
-========================
-
-.. image:: https://img.shields.io/circleci/build/github/xainag/xain-fl/master?style=flat-square
-    :target: https://circleci.com/gh/xainag/xain-fl/tree/master
-
-.. image:: https://img.shields.io/pypi/v/xain-fl?style=flat-square
-    :target: https://pypi.org/project/xain-fl/
-
-.. image:: https://img.shields.io/github/license/xainag/xain-fl?style=flat-square
-    :target: https://github.com/xainag/xain-fl/blob/master/LICENSE
-
-.. image:: https://readthedocs.org/projects/xain/badge/?version=latest&style=flat-square
-    :target: https://docs.xain.io/en/latest/?badge=latest
-    :alt: Documentation Status
-
-**XAIN FL** is an open source framework for federated learning.
-
-
-.. image:: _static/platform-intro.svg
-    :class: intro-image
-
-The XAIN FL project is building a GDPR-compliance layer for machine learning. The
-approach relies on federated machine learning (FedML) as enabling technology
-that removes compliance-related adoption barriers of AI applications used in
-production.
-
-
-User Guide
-----------
+.. mdinclude:: README.md
 
 .. toctree::
    :maxdepth: 2
-
-   intro
-   install
-   quick
+   :hidden:
+   
+   README.md
    network_architecture
-
-
-API Documentation
------------------
-
-.. toctree::
-    :maxdepth: 1
-
-    _code_reference_xain_fl/index
-    _code_reference_benchmarks/index
+   _code_reference_xain_fl/index
diff --git a/docs/install.md b/docs/install.md
deleted file mode 100644
index d218cbd8f..000000000
--- a/docs/install.md
+++ /dev/null
@@ -1,59 +0,0 @@
-# Installation of XAIN
-
-XAIN FL requires [Python 3.6+](https://python.org/).
-
-## Install from PyPi package
-
-To install the `xain-fl` package just run:
-
-```shell
-$ python -m pip install xain-fl
-```
-
-XAIN FL can also be installed with GPU support through the `gpu` extra feature. To
-install the `xain-fl` package with support for GPUs just run:
-
-```shell
-$ python -m pip install xain-fl[gpu]
-```
-
-## Install from source
-
-For development we require some extra system dependencies:
-
-- [clang-format 8+](https://clang.llvm.org/docs/ClangFormat.html)
-  - Linux: `sudo apt install clang-format`
-  - macOS: `brew install clang-format`
-
-### Clone Repository & Install XAIN FL in development mode
-
-To clone this repository and to install the XAIN FL project, please execute the following commands:
-
-```shell
-$ git clone https://github.com/xainag/xain-fl.git
-$ cd xain-fl
-
-$ python -m pip install -e .[dev]
-```
-
-### Verify Installation
-
-You can verify the installation by running the tests
-
-```shell
-$ pytest
-```
-
-### Building the Documentation
-
-The project documentation resides under `docs/`. To build the documentation
-run:
-
-```shell
-$ cd docs/
-$ make docs
-```
-
-The generated documentation will be under `docs/_build/html/`. You can open the
-root of the documentation by opening `docs/_build/html/index.html` on your
-favorite browser.
diff --git a/docs/intro.md b/docs/intro.md
deleted file mode 100644
index ca4b08aa1..000000000
--- a/docs/intro.md
+++ /dev/null
@@ -1,33 +0,0 @@
-# Introduction
-
-The XAIN project is building a GDPR-compliance layer for machine learning. The
-approach relies on federated machine learning (FedML) as enabling technology
-that removes compliance-related adoption barriers of AI applications used in
-production.
-
-At present, the source code in this project demonstrates the effectiveness of
-our FedML implementation on well known benchmarks using a realistic deep
-learning model structure. We will soon add a link to details on those
-experiments.
-
-In the future, we will open source here a first minimal viable product for this
-layer. And we will add links to articles and papers that describe our
-approaches to networking, architecture, and privacy-preserving technology. We
-will also provide references to legal opinions about how and why our compliance
-layer for machine learning meets the demands of GDPR.
-
-POLITE NOTE: We want to point out that running the benchmarks as described
-below is consuming considerable resources. XAIN cannot take any
-responsibilities for costs that arise for you when you execute these demanding
-machine-learning benchmarks.
-
-## Related Papers and Articles
-
-- [An introduction to XAIN’s GDPR-compliance Layer for Machine
-  Learning](https://medium.com/xain/an-introduction-to-xains-gdpr-compliance-layer-for-machine-learning-f7c321b31b06)
-- [Communication-Efficient Learning of Deep Networks from Decentralized
-  Data](https://arxiv.org/abs/1602.05629)
-- [Analyzing Federated Learning through an Adversarial
-  Lens](https://arxiv.org/abs/1811.12470)
-- [Towards Federated Learning at Scale: System
-  Design](https://arxiv.org/abs/1902.01046)
diff --git a/docs/network_architecture.rst b/docs/network_architecture.rst
index 89c2b05a6..9384a88eb 100644
--- a/docs/network_architecture.rst
+++ b/docs/network_architecture.rst
@@ -89,18 +89,18 @@ A **Rendezvous** method that allows *Participants* to register with a
 about the *Participant* in order to keep track of what the *Participant* is
 doing.
 
-A **StartTraining** method that allows *Participants* to get the current global
+A **StartTrainingRound** method that allows *Participants* to get the current global
 model as well as signaling their intent to participate in a given round.
 
-An **EndTraining** method that allows *Participants* to submit their updated
+An **EndTrainingRound** method that allows *Participants* to submit their updated
 models after they finished their training task.
 
 
 In order to remain agnostic to the machine learning framework *Participants*
 and *Coordinator* exchange models in the form of numpy arrays. How models are
 converted from a particular machine learning framework model into numpy arrays
-are outside the scope of this document. We do provide the `Numproto
-<https://github.com/xainag/numproto>`_ python package that performs
+are outside the scope of this document. We do provide the `xain-proto
+<https://github.com/xainag/xain-proto>`_ python package that performs
 serialization and deserialization of numpy arrays into and from protobuf.
 
 
@@ -192,7 +192,7 @@ either:
 
         # rest of the method logic
 
-*Periodically check if a the rcp call is active:*
+*Periodically check if the rpc call is active:*
 
 .. code-block:: python
 
@@ -218,7 +218,7 @@ it's participant list.
 
 .. code-block:: python
 
-    def Hearthbeat(self, request, context):
+    def Heartbeat(self, request, context):
         self.participants[context.peer()].expires = time.now() + KEEPALIVE_TIME
         return PingResponse()
 
@@ -276,12 +276,12 @@ having finished training.
 
 Once all :math:`N` have finished training, :math:`C` collects together all the
 trained data and aggregates them generating a new global model.  It either
-increments the round and repeats, or if there are more rounds to go, it
+increments the round and repeats, or if there are no more rounds to go, it
 transitions to the **FINISHED** state signaling the participants to disconnect.
 
 So far we've only discussed the lifecycle of a *successful* interaction with
 all participants i.e. without faults, dropouts, etc. The true picture (taking
-into account of `fault tolerance <https://hackmd.io/gzGSJZ2xQTyERNjTpqguqg>`_)
+into account `fault tolerance <https://hackmd.io/gzGSJZ2xQTyERNjTpqguqg>`_)
 will be more complicated than above but this is still useful to give the basic
 structure.
 
@@ -300,33 +300,31 @@ single-machine "prototype" :code:`fl/Coordinator`:
 .. code-block:: python
 
     # note: code updated since time of writing but idea remains the same
-    def train_local(p, theta, epochs, epoch_base):
-        theta_update, history = p.train_round(theta, epochs, epoch_base)
+    def train_local(p, weights, epochs, epoch_base):
+        weights_update, history = p.train_round(weights, epochs, epoch_base)
         metrics = p.metrics()
-        return theta_update, history, metrics
+        return weights_update, history, metrics
 
 To do its training, :math:`P` will invoke its own :code:`train_round` function.
 For this, it requires the following data (annotated with their types) from
 :math:`C`
 
-* :code:`theta: KerasWeights` where :code:`KerasWeights = List[ndarray]`
+* :code:`weights: List[ndarray]`
 * :code:`epochs: int`
 * :code:`epoch_base: int`
 
 In return :math:`P` sends back a pair of data
 
-* :code:`theta_update: Tuple[KerasWeights, int]`
-* :code:`history: KerasHistory` where :code:`KerasHistory = Dict[str, List[float]]`
+* :code:`weights_update: Tuple[List[ndarray], int]`
+* :code:`history: Dict[str, List[float]]`
 
 After a :code:`train_round`, :math:`C` also needs from :math:`P`
-a :code:`metrics` of type
-
-* :code:`Metrics = Tuple[int, VolumeByClass]` where :code:`VolumeByClass = List[int]`
+a :code:`metrics` of type :code:`Dict[str, ndarray]`.
 
 .. note::
 
     It is worth bearing in mind that since we are working with gRPC, all service
-    calls must be initiated by the client (as discussed above) i.e. :math:`P`. This
+    calls must be initiated by the client (as discussed above), i.e. :math:`P`. This
     is completely unlike the code excerpt above, where it is naturally :math:`C`
     that calls :math:`P`.
 
@@ -342,52 +340,65 @@ exchanging training data. The Coordinator exposes two service methods
 
 .. code-block:: proto
 
-   rpc StartTraining(StartTrainingRequest) returns (StartTrainingReply) {}
-   rpc EndTraining(EndTrainingRequest) returns (EndTrainingReply) {}
+   rpc StartTrainingRound(StartTrainingRoundRequest) returns (StartTrainingRoundResponse) {}
+   rpc EndTrainingRound(EndTrainingRoundRequest) returns (EndTrainingRoundResponse) {}
 
-where the request and reply data are given as the following protobuf messages:
+where the request and response data are given as the following protobuf messages:
 
 .. code-block:: proto
 
-   message StartTrainingRequest {}
+   message StartTrainingRoundRequest {}
 
-   message StartTrainingReply {
-       repeated numproto.protobuf.NDArray theta = 1;
+   message StartTrainingRoundResponse {
+       repeated xain_proto.numproto.NDArray weights = 1;
        int32 epochs = 2;
        int32 epoch_base = 3;
    }
 
-   message EndTrainingRequest {
-       ThetaUpdate theta_update = 1;
-       map<string, HistoryValue> history = 2;
-       Metrics metrics = 3;  /* also bundled here */
+   message EndTrainingRoundRequest {
+       repeated xain_proto.numproto.NDArray weights = 1;
+       int32 number_samples = 2;
+       map<string, xain_proto.numproto.NDArray> metrics = 3;
    }
 
-   /* message defns for ThetaUpdate, HistoryValue, Metrics omitted */
-
-   message EndTrainingReply {}
+   message EndTrainingRoundResponse {}
 
 
 Note that while most of the Python data types to be exchanged can be
 "protobuf-erized" (and back), :code:`ndarray` requires more work. Fortunately we
-have the `numproto <https://github.com/xainag/numproto>`_ project to help with
-this conversion.
+have the 
+`xain_proto/numproto <https://github.com/xainag/xain-proto/tree/master/python/xain_proto/numproto>`_
+project to help with this conversion.
 
 Training Round Communication
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-The communication is
-summarised in the following sequence diagram.
+The communication is summarised in the following sequence diagram.
 In a training round, :math:`C` is in the state :code:`ROUND`. The selected
 participant :math:`P` is in :code:`TRAINING` state. The first message is by
 :math:`P` essentially kicks off the exchange. :math:`C` responds with the global
-model :math:`\theta` (and other data as specified in
-:code:`StartTrainingReply`). Then :math:`P` carries out the training locally.
-When complete, it sends the updated model :math:`\theta'` (and other metadata)
+model :math:`\weights` (and other data as specified in
+:code:`StartTrainingRoundResponse`). Then :math:`P` carries out the training locally.
+When complete, it sends the updated model :math:`\weights'` (and other metadata)
 back. :math:`C` responds with an acknowledgement.
 
 .. image:: _static/sequence2.png
 
+..
+    this comment is just here to keep the original markdown code of the above sequence diagram!
+    ```sequence
+    participant Coord. as C
+    participant Part. as P
+    Note left of C: ROUND
+    P->C: StartTrainingRound()
+    Note right of P: TRAINING
+    C->P: weights, ...
+    Note right of P: (local training)
+    P->C: EndTrainingRound(weights_update, ...)
+    C->P:
+    Note right of P: POST-TRAINING
+    ```
+
 **Participant Notification via Extended Heartbeat**
 
 In the above, how did :math:`P` detect the state change in :math:`C` to
@@ -427,8 +438,8 @@ After a successful rendezvous, :math:`P` is in **Wait for Selection**. :math:`P`
 this state as long as it keeps receiving :code:`STANDBY` heartbeats. At some round
 :math:`i`, :math:`C` may select :math:`P` for the round by responding with a :code:`ROUND` :math:`i`
 heartbeat. At this point, :math:`P` moves to **Training** where the above sequence of
-training messages (:code:`StartTraining` :math:`\rightarrow \theta \rightarrow \theta'
-\rightarrow` :code:`EndTraining`) occur. Having received the :code:`EndTraining` reply from
+training messages (:code:`StartTrainingRound` :math:`\rightarrow \weights \rightarrow \weights'
+\rightarrow` :code:`EndTrainingRound`) occur. Having received the :code:`EndTrainingRound` response from
 :math:`C`, :math:`P` makes an "internal" transition to **Post-training** where it waits
 until the start of the next round. If it has been selected again, it will
 observe :code:`ROUND` :math:`i+1`. If not, it observes :code:`STANDBY`. Alternatively, if round
diff --git a/docs/quick.md b/docs/quick.md
deleted file mode 100644
index 2490796d9..000000000
--- a/docs/quick.md
+++ /dev/null
@@ -1,137 +0,0 @@
-# Quickstart
-
-## Training
-
-To execute a training session locally:
-
-```shell
-$ python -m benchmarks.benchmark.exec \
-    --group_name=abc \
-    --task_name=def \
-    --dataset=fashion-mnist-100p-iid-balanced \
-    --model=blog_cnn \
-    --R=2 \
-    --E=2 \
-    --C=0.02 \
-    --B=64 \
-    --nopush_results
-```
-
-## Benchmark Suites (using AWS EC2)
-
-Here we describe how to configure and run an AWS service. Please bear in mind
-that you are responsible for any costs that may arise when using these external
-services.
-
-### Configuration
-
-In `~/.aws`, place two config files: `config` and `credentials`. Then execute
-the command:
-
-```shell
-export AWS_PROFILE=xain-xain
-```
-
-### Running a benchmark suite
-
-```shell
-$ python -m benchmarks.benchmark --benchmark_name=BENCHMARK_NAME
-```
-
-You can see valid benchmark names by using
-
-```shell
-$ python -m benchmarks.benchmark --helpfull
-```
-
-### Connect to running instances
-
-Start a benchmark suite
-
-Then:
-
-1. List all running EC2 instances:
-
-```shell
-$ AWS_PROFILE=xain-xain aws ec2 describe-instances  --filters Name=instance-state-code,Values=16 | jq '.Reservations[].Instances[] | "\(.Tags[].Value), \(.PublicIpAddress)"'
-InstanceName1, "35.158.158.119"
-OtherInstanceName2, "18.185.67.166"
-```
-
-2. Connect to one of the running instances using ssh:
-
-```shell
-$ ssh -i ~/.ssh/xain-ec2-remote-training.pem ubuntu@18.185.67.166
-```
-
-3. Follow the logs of the container to see how things are running:
-
-```shell
-$ docker logs -f $(docker ps -q)
-```
-
-## Plotting
-
-To plot final task accuracies in a group of tasks use
-
-```shell
-$ pull_results
-$ aggregate --group_name GROUP_NAME
-```
-
-### Removing obsolete plots
-
-To remove a **single** benchmark result from S3:
-
-```shell
-aws s3 rm --recursive s3://xain-results/[group-name]
-```
-
-To remove **all** benchmark results from S3:
-
-```shell
-aws s3 rm --recursive s3://xain-results
-```
-
-## Ops
-
-Package encapsulates most OPS related tasks.
-
-### Local task
-
-Run a task locally
-
-```python
-from xain_fl.ops import docker, run
-
-image_name = docker.build(should_push=True)
-run.docker(image_name=image_name, benchmark_name="fashion-mnist-100p-iid-balanced")
-```
-
-### Remote task
-
-Run a task on EC2
-
-```python
-from xain_fl.ops import docker, run
-
-image_name = docker.build(should_push=True)
-run.ec2(
-    image_name=image_name,
-    timeout=20,
-    benchmark_name="fashion-mnist-100p-iid-balanced",
-)
-```
-
-## Datasets
-
-
-This modules makes various public datasets available in federated dataset form.
-
-You can find all public methods of the package in its `api` module.
-
-**Example:**
-
-```python
-from xain_fl.datasets.api import cifar10_random_splits_10_load_split
-```
diff --git a/examples/tensorflow_hello_world/helloworld.py b/examples/tensorflow_hello_world/helloworld.py
deleted file mode 100644
index 7884f3290..000000000
--- a/examples/tensorflow_hello_world/helloworld.py
+++ /dev/null
@@ -1,287 +0,0 @@
-import math
-from typing import Callable, List
-
-import tensorflow as tf
-from absl import app, flags
-from tensorflow.keras.layers import Conv2D, Dense, Flatten, Input, MaxPool2D
-
-from xain_fl.datasets import load_splits
-from xain_fl.fl.coordinator import Coordinator, RandomController
-from xain_fl.fl.coordinator.aggregate import FederatedAveragingAgg
-from xain_fl.fl.participant import ModelProvider, Participant
-from xain_fl.types import Partition
-
-# Defining the 'task_name' flag here, to be used by the absl-py app.
-FLAGS = flags.FLAGS
-flags.DEFINE_string("task_name", None, "")
-
-
-DATASET_NAME = "fashion-mnist-100p-iid-balanced"
-"""Specifying a dataset name for this example.
-
-We will use a partitioned version of the Fashion MNIST dataset.
-Please see here: https://xainag.github.io/xain-fl/
-
-100p means that the dataset is split into 100 partitions, which are IID
-Each partition represents the dataset a single client stores locally.
-"""
-
-NUM_CLASSES = 10
-INPUT_SHAPE = (28, 28, 1)
-"""Standard attributes of the Fashion MNIST dataset.
-"""
-
-R = 2
-"""int: Number of global rounds the model is going to be trained for.
-"""
-
-E = 2
-"""int: Number of local epochs.
-
-Each Participant in a round will train the model with its local data for E epochs.
-"""
-
-C = 0.02
-"""float: Fraction of total clients that participate in a training round.
-"""
-
-B = 10
-"""int: Local batch size for a client update.
-"""
-
-
-def main(_):
-    """Main function that runs in the script.
-
-    This function fetches the data split into training partitions, validation and test
-    dataset. A Keras model is then declared and compiled, potential Participants are
-    initiated, as well as the Coordinator. We then call fit and evaluate on the
-    Coordinator and print some basic stats as well as final performance metrics.
-    """
-
-    # Fetching the data.
-    # xy_train_partitions: Each partition is the local training dataset of a single client.
-    # xy_validation: Contains the global validation data (shared by all participants).
-    # xy_test: Contains the global test data (shared by all participants).
-    xy_train_partitions, xy_validation, xy_test = load_splits(DATASET_NAME)
-
-    # Declaring model architecture and compiling Keras model.
-    model_fn = create_and_compile_model
-
-    # Passing model and learning rate functions to ModelProvider.
-    model_provider = ModelProvider(model_fn=model_fn, lr_fn_fn=learning_rate_fn)
-
-    # Initiating the Participant for each client.
-    # At this stage they are not yet selected for training.
-    potential_participants = init_participants(
-        xy_train_partitions=xy_train_partitions,
-        model_provider=model_provider,
-        xy_validation=xy_validation,
-    )
-
-    # Initiating the centralized Coordinator of the training.
-    coordinator = init_coordinator(
-        participants=potential_participants,
-        model_provider=model_provider,
-        xy_validation=xy_validation,
-    )
-
-    # Printing some basic statistics.
-    print_dataset_stats(xy_train_partitions, xy_validation, xy_test)
-
-    # Calling fit on the coordinator starts the training.
-    # It prints validation metrics after R rounds of federated learning are completed.
-    validation_metrics, _, _, _ = coordinator.fit(num_rounds=R)
-    print_validation_metrics(validation_metrics)
-
-    # Evaluating the model with the test set.
-    loss, accuracy = coordinator.evaluate(xy_test)
-
-    # Printing final test loss and accuracy.
-    print(f"---\nTest completed!\nLoss: {loss} | Test accuracy: {accuracy}")
-
-
-def learning_rate_fn(
-    epoch_base: int, lr_initial: float = 0.002, k: float = 0.01
-) -> Callable:
-    """Specifies the learning rate function, in this case with exponential decay.
-
-    Args:
-        epoch_base: Base epoch value.
-        lr_initial: Initial learning rate value.
-        k: Exponential decay constant.
-
-    Returns:
-        Decayed learning rate based on epoch_optimizer.
-    """
-
-    def exp_decay(epoch_optimizer: int) -> float:
-        epoch = epoch_base + epoch_optimizer
-        return lr_initial * math.exp(-k * epoch)
-
-    return exp_decay
-
-
-def create_and_compile_model(epoch_base: int = 0) -> Callable[[], tf.keras.Model]:
-    """Contains the model architecture and compiles it with Keras API.
-
-    Args:
-        epoch_base: Base epoch value.
-
-    Returns:
-        A compiled tf.keras.Model instance, with exponential learning rate decay.
-    """
-
-    def add_convolution(filters, kernel_inizializer):
-        convolution = Conv2D(
-            filters,
-            kernel_size=(5, 5),
-            strides=(1, 1),
-            kernel_initializer=kernel_inizializer,
-            padding="same",
-            activation="relu",
-        )
-        return convolution
-
-    ki = tf.keras.initializers.glorot_uniform(seed=42)
-
-    inputs = Input(shape=INPUT_SHAPE)
-    x = add_convolution(filters=32, kernel_inizializer=ki)(inputs)
-    x = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)
-    x = add_convolution(filters=64, kernel_inizializer=ki)(x)
-    x = MaxPool2D(pool_size=(2, 2), strides=(2, 2))(x)
-    x = Flatten()(x)
-    x = Dense(512, kernel_initializer=ki, activation="relu")(x)
-    outputs = Dense(NUM_CLASSES, kernel_initializer=ki, activation="softmax")(x)
-
-    model = tf.keras.Model(inputs=inputs, outputs=outputs)
-
-    lr_fn = learning_rate_fn(epoch_base=epoch_base)
-    optimizer = tf.keras.optimizers.Adam(lr=lr_fn(0))
-
-    model.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=optimizer,
-        metrics=["accuracy"],
-    )
-    return model
-
-
-def init_participants(
-    xy_train_partitions: List[Partition],
-    model_provider: ModelProvider,
-    xy_validation: Partition,
-) -> List[Participant]:
-    """Initiates potential Participants.
-
-    Iterate through each partition (a client's training data) and
-    initiates a Participant instance.
-
-    Args:
-        xy_train_partitions: Partitioned training data.
-        model_provider: ModelProvider instance holding model and
-            learning rate functions.
-        xy_validation: Validation dataset.
-
-    Returns:
-        A list of initiated Participant instances, one per client.
-    """
-
-    potential_participants = []
-    for client_id, xy_train in enumerate(xy_train_partitions):
-        participant = Participant(
-            cid=client_id,
-            model_provider=model_provider,
-            xy_train=xy_train,
-            xy_val=xy_validation,
-            num_classes=NUM_CLASSES,
-            batch_size=B,
-        )
-        potential_participants.append(participant)
-    return potential_participants
-
-
-def init_coordinator(
-    participants: List[Participant],
-    model_provider: ModelProvider,
-    xy_validation: Partition,
-) -> Coordinator:
-    """Initiates the Coordinator.
-
-    The controller will select the indices of the clients participating in the training.
-    It is initialized with all the indices of the clients available.
-
-    Federated Averaging is a common aggregation method.
-    See here for more details: https://arxiv.org/pdf/1602.05629.pdf
-
-    The Coordinator will coordinate the training. It will:
-    - Select a C fraction of potential participants.
-    - Send training jobs to each selected Participant,
-    who will train its own local data for E local epochs.
-
-    Args:
-        participants: List of initiated Participant instances, one per client.
-        model_provider: ModelProvider instance holding model and learning rate functions.
-        xy_validation: Validation dataset.
-
-    Returns:
-        Initiated Coordinator instance.
-    """
-    participant_ids = [str(participant.cid) for participant in participants]
-    controller = RandomController(participant_ids, fraction_of_participants=C)
-
-    aggregator = FederatedAveragingAgg()
-
-    coordinator = Coordinator(
-        controller=controller,
-        model_provider=model_provider,
-        participants=participants,
-        C=C,
-        E=E,
-        xy_val=xy_validation,
-        aggregator=aggregator,
-    )
-    return coordinator
-
-
-def print_dataset_stats(
-    xy_train_partitions: List[Partition], xy_validation: Partition, xy_test: Partition
-):
-    """Prints some preliminary stats to the terminal.
-
-    Args:
-        xy_train_partitions: Partitioned training data.
-        xy_validation: Validation dataset.
-        xy_test: Test dataset.
-    """
-
-    print(f"\nthere are {len(xy_train_partitions)} client/potential partitions")
-    images_first_client, labels_first_client = xy_train_partitions[0]
-    n_images, height, width = images_first_client.shape
-    print(f"the first client has {n_images} images, of {width}x{height} size")
-    print(f"there are {len(labels_first_client)} labels, one label per image")
-
-    validation_images, _ = xy_validation
-    print(f"---\nthe validation set is made of {len(validation_images)} images")
-
-    test_images, _ = xy_test
-    print(f"the test set is made of {len(test_images)} images\n---")
-
-
-def print_validation_metrics(validation_metrics: dict):
-    """Prints the validation metrics to the terminal.
-
-    Args:
-        validation_metrics: Dictionary containing loss and accuracy per each round.
-    """
-
-    for round_id in range(R):
-        print(
-            f"validation round: {round_id + 1}: "
-            f"loss: {validation_metrics['val_loss'][round_id]}, "
-            f"accuracy: {validation_metrics['val_acc'][round_id]}"
-        )
-
-
-if __name__ == "__main__":
-    app.run(main=main)
diff --git a/mypy.ini b/mypy.ini
index 19d1da725..23d20f36d 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,5 +1,5 @@
 [mypy]
 ignore_missing_imports = True
 
-[mypy-xain_fl.cproto.participant]
+[mypy-xain_fl.coordinator.legacy_participant]
 ignore_errors = True
diff --git a/output/.gitkeep b/output/.gitkeep
deleted file mode 100644
index e69de29bb..000000000
diff --git a/protobuf/xain_fl/cproto/coordinator.proto b/protobuf/xain_fl/cproto/coordinator.proto
deleted file mode 100644
index 41088c094..000000000
--- a/protobuf/xain_fl/cproto/coordinator.proto
+++ /dev/null
@@ -1,66 +0,0 @@
-syntax = "proto3";
-
-import "numproto/protobuf/ndarray.proto";
-
-package xain.protobuf.coordinator;
-
-service Coordinator {
-  rpc Rendezvous(RendezvousRequest) returns (RendezvousReply) {}
-  rpc Heartbeat(HeartbeatRequest) returns (HeartbeatReply) {}
-  rpc StartTraining(StartTrainingRequest) returns (StartTrainingReply) {}
-  rpc EndTraining(EndTrainingRequest) returns (EndTrainingReply) {}
-}
-
-enum RendezvousResponse {
-  ACCEPT = 0;
-  LATER = 1;
-}
-
-message RendezvousRequest {}
-
-message RendezvousReply {
-  RendezvousResponse response = 1;
-}
-
-enum State {
-  // Set by the coordinator to signal there is no round in progress
-  STANDBY = 0;
-  // Set by the coordinator to signal what round is currently in progress
-  ROUND = 1;
-  // Set by the coordinator to signal that the session is over
-  FINISHED = 2;
-  // Set by the participant to signal that it is ready for work
-  READY = 3;
-  // Set by the participant to signal that it is currently training
-  TRAINING = 4;
-}
-
-message HeartbeatRequest {
-  State state = 1;
-  int32 round = 2;
-}
-
-message HeartbeatReply {
-  State state = 1;
-  int32 round = 2;
-}
-
-message StartTrainingRequest {}
-
-message StartTrainingReply {
-  repeated numproto.protobuf.NDArray weights = 1;
-  int32 epochs = 2;
-  int32 epoch_base = 3;
-}
-
-message EndTrainingRequest {
-  repeated numproto.protobuf.NDArray weights = 1;
-  int32 number_samples = 2;
-  map<string, Metrics> metrics = 3;
-
-  message Metrics {
-    repeated numproto.protobuf.NDArray metrics = 1;
-  }
-}
-
-message EndTrainingReply {}
diff --git a/protobuf/xain_fl/cproto/hellonumproto.proto b/protobuf/xain_fl/cproto/hellonumproto.proto
deleted file mode 100644
index ad096a8c4..000000000
--- a/protobuf/xain_fl/cproto/hellonumproto.proto
+++ /dev/null
@@ -1,17 +0,0 @@
-syntax = "proto3";
-
-import "numproto/protobuf/ndarray.proto";
-
-package hellonumproto;
-
-service NumProtoServer {
-  rpc SayHelloNumProto(NumProtoRequest) returns (NumProtoReply) {}
-}
-
-message NumProtoRequest {
-  numproto.protobuf.NDArray arr = 1;
-}
-
-message NumProtoReply {
-  numproto.protobuf.NDArray arr = 1;
-}
diff --git a/pylint.ini b/pylint.ini
deleted file mode 100644
index ca59b4d42..000000000
--- a/pylint.ini
+++ /dev/null
@@ -1,21 +0,0 @@
-[MESSAGES CONTROL]
-
-# C0103: Invalid constant name (invalid-name)
-# C0111: Missing module docstring (missing-docstring)
-# C0111: Missing function docstring (missing-docstring)
-# C0330: Wrong hanging indentation before block
-# E0401: import-error
-# R0801: duplicate-code
-# R0913: too-many-arguments
-# R0914: too-many-locals
-disable=C0103,C0111,C0330,C0412,E0401,W0511,R0903,R0801,R0913,R0914
-
-[TYPECHECK]
-# List of members which are set dynamically and missed by pylint inference
-# system, and so shouldn't trigger E1101 when accessed. Python regular
-# expressions are accepted.
-# See: https://github.com/pytorch/pytorch/issues/701#issuecomment-378048755
-generated-members=numpy.*,torch.*
-
-# ignore auto generated grpc files
-ignore-patterns=.*_pb2.*
diff --git a/pytest.ini b/pytest.ini
index f92f55666..d739e1b6d 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,5 +1,5 @@
 [pytest]
-testpaths = benchmarks xain_fl
+testpaths = tests
 markers =
     unmarked: every test without a marker will automatically have this marker assigned. You could senselessly add it manually
     integration: mark a integration test
diff --git a/scripts/create_git_hooks.sh b/scripts/create_git_hooks.sh
index bc87ce78c..b0963732c 100755
--- a/scripts/create_git_hooks.sh
+++ b/scripts/create_git_hooks.sh
@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 # Exit immediately if a command exits with a non-zero status.
 set -e
@@ -11,10 +11,10 @@ cd $DIR/../
 
 if [ -f "$PRE_PUSH_HOOK" ]; then
     echo "$PRE_PUSH_HOOK exist"
-else 
+else
     echo "#!/bin/sh" >> $PRE_PUSH_HOOK
     echo "./scripts/test.sh" >> $PRE_PUSH_HOOK
-    
+
     chmod +x $PRE_PUSH_HOOK
 
     echo "$PRE_PUSH_HOOK created"
diff --git a/scripts/format.sh b/scripts/format.sh
index 1adcbc4e0..aa61c84f7 100755
--- a/scripts/format.sh
+++ b/scripts/format.sh
@@ -1,11 +1,10 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
-# Exit immediately if a command exits with a non-zero status.
+# Exit immediately if a command exits with a non-zero status
 set -e
-
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
 cd $DIR/../
 
-isort --indent=4 -rc setup.py conftest.py benchmarks examples xain_fl docs/conf.py
-black --exclude "xain_fl/cproto/.*_pb2.*" setup.py conftest.py benchmarks examples xain_fl docs/conf.py
+set -x
+isort --indent=4 -rc setup.py xain_fl tests
+black --line-length 100 setup.py xain_fl tests
diff --git a/scripts/ls_running_instances.sh b/scripts/ls_running_instances.sh
deleted file mode 100755
index ea9796c32..000000000
--- a/scripts/ls_running_instances.sh
+++ /dev/null
@@ -1,6 +0,0 @@
-#!/bin/bash
-set -e  # Exit immediately if a command exits with a non-zero status
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-cd $DIR/../
-
-AWS_PROFILE=xain-xain aws ec2 describe-instances  --filters Name=instance-state-code,Values=16 | jq '.Reservations[].Instances[] | "\(.Tags[].Value), \(.PublicIpAddress)"'
diff --git a/scripts/rm_caches.sh b/scripts/rm_caches.sh
index a71fb1bfe..d2baaacaa 100755
--- a/scripts/rm_caches.sh
+++ b/scripts/rm_caches.sh
@@ -1,10 +1,13 @@
-#!/bin/bash
+#!/usr/bin/env bash
+
 set -e  # Exit immediately if a command exits with a non-zero status
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 cd $DIR/../
 
+set -x
 rm -rf .mypy_cache
 rm -rf .pytest_cache
-find . -type d -name __pycache__ -exec rm -r {} \+
+rm -rf __pycache__
+find xain_fl tests -type d -name __pycache__ -exec rm -r {} \+
 rm -rf docs/_code_reference_*
 rm -rf docs/_build
diff --git a/scripts/setup-examples.sh b/scripts/setup-examples.sh
deleted file mode 100644
index 45f5e11e9..000000000
--- a/scripts/setup-examples.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-cd $DIR/../
-
-python -m pip install -U pip==19.3.1
-python -m pip install -U setuptools==41.6.0
-python -m pip install .[examples]
diff --git a/scripts/setup.sh b/scripts/setup.sh
deleted file mode 100755
index aa2efc2a5..000000000
--- a/scripts/setup.sh
+++ /dev/null
@@ -1,12 +0,0 @@
-#!/bin/bash
-
-# Exit immediately if a command exits with a non-zero status.
-set -e
-
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-cd $DIR/../
-
-python -m pip install -U pip==19.3.1
-python -m pip install -U setuptools==41.6.0
-python -m pip install -e .[dev]
diff --git a/scripts/setup_pyenv.sh b/scripts/setup_pyenv.sh
deleted file mode 100755
index 06b4bad98..000000000
--- a/scripts/setup_pyenv.sh
+++ /dev/null
@@ -1,64 +0,0 @@
-#!/bin/bash
-set -e  # Exit immediately if a command exits with a non-zero status
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-cd $DIR/../
-
-PROJECT_DIR="$( pwd | awk 'BEGIN { FS = "/" } ; { print $NF }' )"
-UNAME_OUT="$(uname -s)"
-
-# Check if `git` is installed
-if ! [ -x "$(command -v git)" ]; then
-  echo 'Error: git is not installed.' >&2
-  exit 1
-fi
-
-# Check if `brew` is installed on macOS
-if [ $UNAME_OUT == "Darwin" ]; then
-    if ! [ -x "$(command -v brew)" ]; then
-        echo 'Error: git is not installed.' >&2
-        exit 1
-    fi
-fi
-
-case $UNAME_OUT in
-    Linux*)     CONFIG_FILE=".bashrc";;
-    Darwin*)    CONFIG_FILE=".zshenv";;  # macOS 10.15+ defaults to ZSH
-    *)          exit 1
-esac
-
-# Install for macOS
-if [ $UNAME_OUT == "Darwin" ]; then
-    brew install openssl readline sqlite3 xz zlib
-    # sudo installer -pkg /Library/Developer/CommandLineTools/Packages/macOS_SDK_headers_for_macOS_10.14.pkg -allowUntrusted -target /
-fi
-
-# Install for Ubuntu/Debian/Mint
-if [ $UNAME_OUT == "Linux" ]; then
-    su -c "apt-get update"
-    su -c "DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev"
-fi
-
-# Setup pyenv
-git clone https://github.com/pyenv/pyenv.git ~/.pyenv
-echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/$CONFIG_FILE
-echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/$CONFIG_FILE
-echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi' >> ~/$CONFIG_FILE
-
-PYENV_ROOT="$HOME/.pyenv"
-PATH="$PYENV_ROOT/bin:$PATH"
-
-# Setup pyenv-virtualenv
-git clone https://github.com/pyenv/pyenv-virtualenv.git $(pyenv root)/plugins/pyenv-virtualenv
-echo 'eval "$(pyenv virtualenv-init -)"' >> ~/$CONFIG_FILE
-
-# Install relevant CPython versions
-pyenv install 3.6.9
-pyenv install 3.7.5
-pyenv install 3.8.0
-
-# Creates project specific virtualenv
-pyenv virtualenv 3.6.9 ${PROJECT_DIR}-3.6.9
-
-# Create .python-version project file
-echo "${PROJECT_DIR}-3.6.9" >> .python-version
diff --git a/scripts/test.sh b/scripts/test.sh
index 26d37ebbd..51eb357d4 100755
--- a/scripts/test.sh
+++ b/scripts/test.sh
@@ -1,39 +1,20 @@
-#!/bin/bash
-
-# clang-format does not provide a way to check the files.
-# This function iterates over a list of files and checks each one of them
-# for formatting errors
-clang_format() {
-    local_ret=0
-
-    for f in ./protobuf/xain_fl/cproto/*.proto
-    do
-        echo "Processing $f"
-        clang-format -style="{Language: Proto, BasedOnStyle: Google}" $f | diff $f -
-
-        if [ $? -ne 0 ] ; then
-            local local_ret=1
-        fi
-
-    done
-    return $local_ret
-}
+#!/usr/bin/env bash
 
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 
 cd $DIR/../
 
 # sort import
-isort --check-only --indent=4 -rc setup.py conftest.py benchmarks examples xain_fl && echo "===> isort says: well done <===" &&
+isort --check-only --indent=4 -rc setup.py xain_fl tests && echo "===> isort says: well done <===" &&
 
 # format code
-black --check --exclude "xain_fl/cproto/.*_pb2.*" setup.py conftest.py benchmarks examples xain_fl && echo "===> black says: well done <===" &&
+black --line-length 100 --check setup.py xain_fl tests && echo "===> black says: well done <===" &&
 
 # lint
-pylint --rcfile=pylint.ini benchmarks examples xain_fl && echo "===> pylint says: well done <===" &&
+pylint --rcfile=.pylintrc xain_fl tests && echo "===> pylint says: well done <===" &&
 
 # type checks
-mypy benchmarks examples/* xain_fl && echo "===> mypy says: well done <===" &&
+mypy xain_fl tests && echo "===> mypy says: well done <===" &&
 
 # documentation checks
 (cd docs/ && SPHINXOPTS="-W" make docs) && echo "===> sphinx-build says: well done <===" &&
diff --git a/scripts/test_slow.sh b/scripts/test_slow.sh
deleted file mode 100755
index cd903db84..000000000
--- a/scripts/test_slow.sh
+++ /dev/null
@@ -1,8 +0,0 @@
-#!/bin/bash
-
-DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-
-cd $DIR/../
-
-# tests
-pytest -v -m slow --runslow
diff --git a/setup.py b/setup.py
index d5e1976a7..cfc573340 100644
--- a/setup.py
+++ b/setup.py
@@ -1,10 +1,7 @@
-import glob
 import os.path
-import pathlib
 import sys
 
 from setuptools import find_packages, setup
-from setuptools.command.develop import develop
 
 if sys.version_info < (3, 6):
     sys.exit("Please use Python version 3.6 or higher.")
@@ -24,90 +21,41 @@
     readme = fp.read()
 
 
-# Handle protobuf
-class CustomDevelopCommand(develop):
-    def run(self):
-        # we need to import this here or else these packages would have to be
-        # installed in the system before we could run the setup.py
-        import numproto
-        import grpc_tools
-        from grpc_tools import protoc
-
-        develop.run(self)
-
-        # get the path of the numproto protofiles
-        # this will give us the path to the site-packages where numproto is
-        # installed
-        numproto_path = pathlib.Path(numproto.__path__[0]).parent
-
-        # get the path of grpc_tools protofiles
-        grpc_path = grpc_tools.__path__[0]
-
-        proto_files = glob.glob("./protobuf/xain_fl/cproto/*.proto")
-        command = [
-            "grpc_tools.protoc",
-            # path to numproto .proto files
-            f"--proto_path={numproto_path}",
-            # path to google .proto fiels
-            f"--proto_path={grpc_path}/_proto",
-            "--proto_path=./protobuf",
-            "--python_out=./",
-            "--grpc_python_out=./",
-            "--mypy_out=./",
-        ] + proto_files
-
-        print("Building proto_files {}".format(proto_files))
-        if protoc.main(command) != 0:
-            raise Exception("error: {} failed".format(command))
-
-
 # License comments according to `pip-licenses`
 
 install_requires = [
-    "typing-extensions~=3.7",  # PSF
-    "numpy~=1.15",  # BSD
-    "absl-py~=0.8",  # Apache 2.0
-    "grpcio~=1.23",  # Apache License 2.0
-    "protobuf~=3.9",  # 3-Clause BSD License
-    "numproto~=0.3",  # Apache License 2.0
-    "requests==2.22.0",  # Apache 2.0  # TODO(XP-185) remove
-    "tensorflow==1.14.0",  # Apache 2.0  # TODO(XP-131) remove
+    "numpy==1.15",  # BSD
+    "grpcio==1.23",  # Apache License 2.0
     "structlog==19.2.0",  # Apache License 2.0
+    "xain-proto==0.3.0",  # Apache License 2.0
+    "boto3==1.10.48",  # Apache License 2.0
 ]
 
-benchmarks_require = [
-    "matplotlib==3.1.1",  # PSF
-    "botocore==1.12.220",  # Apache License 2.0
-    "boto3==1.9.220",  # Apache License 2.0
-    "awscli==1.16.230",  # Apache License 2.0
-    "faker==2.0.0",  # MIT License
-]
-
-gpu_require = ["tensorflow-gpu==1.14.0"]  # Apache 2.0
-
 dev_require = [
-    "grpcio-tools~=1.23",  # Apache License 2.0
-    "black==19.3b0",  # MIT
-    "mypy==0.720",  # MIT License
+    "black==19.10b0",  # MIT
+    "mypy==0.760",  # MIT License
     "pylint==2.3.1",  # GPL
-    "astroid<=2.2.5",  # LGPL
-    "isort==4.3.20",  # MIT
-    "rope==0.14.0",  # GNU GPL
+    "astroid==2.2.5",  # LGPL
+    "isort==4.3.21",  # MIT
     "pip-licenses==1.15.2",  # MIT License
-    "mypy-protobuf==1.15",  # Apache License 2.0
     "twine==2.0.0",  # Apache License 2.0
     "wheel==0.33.6",  # MIT
+    "typing-extensions==3.7.4.1",  # PSF
 ]
 
-examples_require = ["tensorflow==1.14.0"]  # Apache 2.0
-
 tests_require = [
-    "pytest==4.6.2",  # MIT license
-    "pytest-cov==2.7.1",  # MIT
+    "pytest==5.3.2",  # MIT license
+    "pytest-cov==2.8.1",  # MIT
     "pytest-watch==4.2.0",  # MIT
+    "xain-sdk==0.3.0",  # Apache License 2.0
 ]
 
-docs_require = ["Sphinx==2.2.1", "recommonmark==0.6.0", "sphinxcontrib-mermaid==0.3.1"]
+docs_require = [
+    "Sphinx==2.2.1",
+    "m2r==0.2.1",
+    "sphinxcontrib-mermaid==0.3.1",
+    "sphinx-autodoc-typehints==1.10.3",  # MIT
+]
 
 setup(
     name="xain_fl",
@@ -116,10 +64,7 @@ def run(self):
     long_description=readme,
     long_description_content_type="text/markdown",
     url="https://github.com/xainag/xain-fl",
-    author=[
-        "Daniel J. Beutel <daniel.beutel@xain.io>",
-        "Taner Topal <taner.topal@xain.io>",
-    ],
+    author=["XAIN AG"],
     author_email="services@xain.io",
     license="Apache License Version 2.0",
     zip_safe=False,
@@ -143,30 +88,13 @@ def run(self):
         "Operating System :: MacOS :: MacOS X",
         "Operating System :: POSIX :: Linux",
     ],
-    packages=find_packages(
-        where=".", exclude=["benchmarks", "benchmarks.*", "*_test.py"]
-    ),
+    packages=find_packages(exclude=["tests"]),
     install_requires=install_requires,
     tests_require=tests_require,
     extras_require={
         "test": tests_require,
-        "gpu": gpu_require,
         "docs": docs_require,
-        "benchmarks": benchmarks_require,
-        "examples": examples_require,
-        "dev": dev_require
-        + tests_require
-        + benchmarks_require
-        + docs_require
-        + examples_require,
-    },
-    cmdclass={"develop": CustomDevelopCommand},
-    entry_points={
-        "console_scripts": [
-            "coordinator=xain_fl.cproto.cli:main",
-            "train_remote=benchmarks.train_remote:main",
-            "pull_results=xain_fl.ops.__main__:download",
-            "aggregate=benchmarks.aggregate:main",
-        ]
+        "dev": dev_require + tests_require + docs_require,
     },
+    entry_points={"console_scripts": ["coordinator=xain_fl.cli:main"]},
 )
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 000000000..dd0638502
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1 @@
+"""XAIN FL tests"""
diff --git a/xain_fl/fl/coordinator/aggregate_test.py b/tests/aggregate_test.py
similarity index 69%
rename from xain_fl/fl/coordinator/aggregate_test.py
rename to tests/aggregate_test.py
index 8e09e5325..df2adfa60 100644
--- a/xain_fl/fl/coordinator/aggregate_test.py
+++ b/tests/aggregate_test.py
@@ -1,9 +1,16 @@
+"""XAIN FL tests for coordinator aggregation"""
+
 import numpy as np
 
-from . import aggregate
+from xain_fl.fl.coordinator.aggregate import federated_averaging
 
 
 def test_federated_averaging():  # pylint: disable=too-many-locals
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
     # Prepare:
     # - Three weight updates (u0, u1, u2)
     # - One layer in the model
@@ -11,17 +18,17 @@ def test_federated_averaging():  # pylint: disable=too-many-locals
 
     u0_l1_w0 = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
     u0_l1_w1 = np.ones((2))
-    u0 = [u0_l1_w0, u0_l1_w1]
+    u0_ = [u0_l1_w0, u0_l1_w1]
 
     u1_l1_w0 = np.array([[2.0, 3.0, 1.0], [4.0, 5.0, 6.0]])
     u1_l1_w1 = np.ones((2))
-    u1 = [u1_l1_w0, u1_l1_w1]
+    u1_ = [u1_l1_w0, u1_l1_w1]
 
     u2_l1_w0 = np.array([[3.0, 1.0, 2.0], [4.0, 5.0, 6.0]])
     u2_l1_w1 = np.ones((2))
-    u2 = [u2_l1_w0, u2_l1_w1]
+    u2_ = [u2_l1_w0, u2_l1_w1]
 
-    thetas = [u0, u1, u2]
+    thetas = [u0_, u1_, u2_]
 
     theta_expected = [
         np.array([[2.0, 2.0, 2.0], [4.0, 5.0, 6.0]]),
@@ -31,7 +38,7 @@ def test_federated_averaging():  # pylint: disable=too-many-locals
     weighting = np.ones((len(thetas)))
 
     # Execute
-    theta_actual = aggregate.federated_averaging(thetas, weighting)
+    theta_actual = federated_averaging(thetas, weighting)
 
     # Assert
     assert len(theta_actual) == len(theta_expected)
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 000000000..39f43b45b
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,142 @@
+"""XAIN FL conftest for cproto"""
+
+from concurrent import futures
+import threading
+from unittest import mock
+
+import grpc
+import pytest
+from xain_proto.fl import coordinator_pb2_grpc, hellonumproto_pb2_grpc
+
+from xain_fl.coordinator.coordinator import Coordinator
+from xain_fl.coordinator.coordinator_grpc import CoordinatorGrpc
+from xain_fl.coordinator.heartbeat import monitor_heartbeats
+from xain_fl.fl.coordinator.aggregate import ModelSumAgg
+from xain_fl.fl.coordinator.controller import IdController
+from xain_fl.helloproto.numproto_server import NumProtoServer
+
+from .port_forwarding import ConnectionManager
+from .store import TestStore
+
+
+@pytest.fixture
+def greeter_server():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    hellonumproto_pb2_grpc.add_NumProtoServerServicer_to_server(NumProtoServer(), server)
+    server.add_insecure_port("localhost:50051")
+    server.start()
+    yield
+    server.stop(0)
+
+
+@pytest.fixture
+def coordinator_service():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    coordinator = Coordinator(minimum_participants_in_round=10, fraction_of_participants=1.0)
+    store = TestStore()
+    coordinator_grpc = CoordinatorGrpc(coordinator, store)
+    coordinator_pb2_grpc.add_CoordinatorServicer_to_server(coordinator_grpc, server)
+    server.add_insecure_port("localhost:50051")
+    server.start()
+    yield coordinator_grpc
+    server.stop(0)
+
+
+@pytest.fixture
+def mock_coordinator_service():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    agg = ModelSumAgg()
+    ctrl = IdController()
+    coordinator = Coordinator(
+        num_rounds=2,
+        minimum_participants_in_round=1,
+        fraction_of_participants=1.0,
+        aggregator=agg,
+        controller=ctrl,
+    )
+    with mock.patch("xain_fl.coordinator.coordinator_grpc.Store") as mock_obj:
+        mock_store = mock_obj.return_value
+        coordinator_grpc = CoordinatorGrpc(coordinator, mock_store)
+        coordinator_pb2_grpc.add_CoordinatorServicer_to_server(coordinator_grpc, server)
+        server.add_insecure_port("localhost:50051")
+        server.start()
+        terminate_event = threading.Event()
+        monitor_thread = threading.Thread(
+            target=monitor_heartbeats, args=(coordinator, terminate_event)
+        )
+        monitor_thread.start()
+        yield coordinator_grpc
+        terminate_event.set()
+        monitor_thread.join()
+        server.stop(0)
+
+
+@pytest.fixture
+def participant_stub():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Returns:
+        [type]: [description]
+    """
+
+    channel = grpc.insecure_channel("localhost:50051")
+    stub = coordinator_pb2_grpc.CoordinatorStub(channel)
+
+    yield stub
+
+    channel.close()
+
+
+def port_generator():
+    """A generator that yields incrementing port numbers.
+
+    """
+    port = 50052
+    while True:
+        yield port
+        port += 1
+
+
+@pytest.fixture
+def participant_stubs():
+    """Generator that yields functions instantiate participant stubs.
+    Each participant creates a new TCP connection, so that they get a
+    different participant ID.
+
+    """
+
+    ports = port_generator()
+    connections = ConnectionManager()
+    channels = []
+
+    def generate_participant_stubs():
+        for port in ports:
+            connections.start("localhost", port, "localhost", 50051)
+            channel = grpc.insecure_channel(f"localhost:{port}")
+            channels.append(channel)
+            stub = coordinator_pb2_grpc.CoordinatorStub(channel)
+            yield stub
+
+    yield generate_participant_stubs()
+
+    for channel in channels:
+        channel.close()
+
+    connections.stop_all()
diff --git a/xain_fl/fl/coordinator/controller_test.py b/tests/controller_test.py
similarity index 71%
rename from xain_fl/fl/coordinator/controller_test.py
rename to tests/controller_test.py
index 449d88642..72474336d 100644
--- a/xain_fl/fl/coordinator/controller_test.py
+++ b/tests/controller_test.py
@@ -1,3 +1,5 @@
+"""XAIN FL tests for controller"""
+
 import numpy as np
 
 from xain_fl.fl.coordinator.controller import RandomController
@@ -9,14 +11,10 @@ def test_random_controller():
     """
     participant_ids = ["a", "b", "c", "d", "e", "f", "g"]
     fractions = np.arange(0.25, 1, 0.25)
-    expected_lengths = [
-        np.ceil(fraction * len(participant_ids)) for fraction in fractions
-    ]
+    expected_lengths = [np.ceil(fraction * len(participant_ids)) for fraction in fractions]
     for fraction, expected_length in zip(fractions, expected_lengths):
-        controller = RandomController(
-            participant_ids, fraction_of_participants=fraction
-        )
-        ids = controller.select_ids()
+        controller = RandomController(fraction_of_participants=fraction)
+        ids = controller.select_ids(participant_ids)
         set_ids = set(ids)
 
         # check that length of set_ids is as expected
diff --git a/tests/port_forwarding.py b/tests/port_forwarding.py
new file mode 100644
index 000000000..b3af87530
--- /dev/null
+++ b/tests/port_forwarding.py
@@ -0,0 +1,205 @@
+"""
+This module provides logic to perform port forwarding. We use this to
+artificially open new TCP connections when creating gRPC clients.
+"""
+from errno import EBADF, ECONNRESET, ENOTCONN
+import faulthandler
+import socket
+from threading import Event, Thread
+
+# Buffer size for reading from a connection
+BUFFER_SIZE = 4096
+
+
+def transfer_worker(src, dst, terminate_event):
+    """A worker that reads data from the `src` socket and forwards it to
+    the `dst` socket.
+
+    Args:
+
+        src (socket.socket): source socket
+        dst (socket.socket): destination socket
+        terminate_event (Event): event used to tell the caller that
+            the `src` socket is closed
+
+    """
+    while True:
+        try:
+            # Block until there's data to read OR the connection
+            # closes.
+            data = src.recv(BUFFER_SIZE)
+        except OSError as exc:
+            if exc.errno in [ECONNRESET, EBADF, ENOTCONN]:
+                terminate_event.set()
+                return
+            raise
+
+        # The socket is closed
+        if not data:
+            terminate_event.set()
+            return
+
+        try:
+            dst.send(data)
+        except OSError as exc:
+            if exc.errno in [ECONNRESET, EBADF, ENOTCONN]:
+                terminate_event.set()
+                return
+            raise
+
+
+def forward(host, port, target_host, target_port, terminate_event):
+    """Set up a TCP socket listening on host:port. Once a connection is
+    established, open a connection to target_host:target_port and
+    forward data both way.
+
+    # Example
+
+    ```python
+    >>> forward("localhost", 8080, "localhost", 80)
+    ```
+
+    has the same effect than:
+
+    ```shell
+    socat tcp-listen:8080,reuseaddr,fork tcp:localhost:80
+    ```
+
+    Args:
+
+        host (str): hostname or ip address for listening
+        port (str): port number for listening
+        target_host (str): hostname of ip address to establish a
+            connection with
+        target_port (str): port number to establish a connection to
+            connection with
+        terminate_event (Event): when this event is set, this function:
+            - closes the sockets it opened
+            - waits for the two `transfer_worker()` threads to finish
+            - returns
+    """
+    # Set up a server socket that will wait for incoming connections
+    server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+    # Make the socket non-blocking so that `accept()` doesn't block
+    # forever, and we can periodically check whether the
+    # `terminate_event` is set.
+    server.settimeout(0.1)
+    server.bind((host, port))
+    # Allow only one connection
+    server.listen(1)
+
+    # Block until the first client connects or we're told to terminate
+    while True:
+        try:
+            client_conn, _ = server.accept()
+        except socket.timeout:
+            # Check whether we should terminate
+            if terminate_event.is_set():
+                server.close()
+                return
+        else:
+            break
+
+    # Once the client is connected, open a connection with the target
+    target_conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    target_conn.connect((target_host, target_port))
+
+    # Start transfering data both way
+    tx_thread = Thread(
+        target=transfer_worker,
+        name=f"{target_host}:{target_port}->{host}:{port}",
+        args=(target_conn, client_conn, terminate_event),
+        daemon=True,
+    )
+    rx_thread = Thread(
+        target=transfer_worker,
+        name=f"{host}:{port}->{target_host}:{target_port}",
+        args=(client_conn, target_conn, terminate_event),
+        daemon=True,
+    )
+    tx_thread.start()
+    rx_thread.start()
+
+    # Wait until we're told to terminate (this event can be fired for
+    # the `transfer_worker()` threads, or by the caller.
+    _ = terminate_event.wait()
+
+    # Close all the sockets
+    for sock in [target_conn, client_conn, server]:
+        try:
+            sock.shutdown(socket.SHUT_RDWR)
+        except OSError as exc:
+            if exc.errno not in [ECONNRESET, EBADF, ENOTCONN]:
+                raise
+        sock.close()
+
+    # Wait for the transfer workers to terminate
+    tx_thread.join(timeout=0.2)
+    assert not tx_thread.isAlive()
+    rx_thread.join(timeout=0.2)
+    assert not rx_thread.isAlive()
+
+
+class ConnectionManager:
+    """
+    Manage multiple forwarding workers.
+    """
+
+    def __init__(self):
+        self.forwarders = {}
+
+    def start(self, host, port, target_host, target_port):
+        """Start a new forwarding worker.
+
+        Args:
+
+            host (str): hostname or ip address for listening
+            port (str): port number for listening
+            target_host (str): hostname of ip address to establish a
+                connection with
+            target_port (str): port number to establish a connection
+                to connection with
+
+        """
+        terminate_event = Event()
+        thread = Thread(
+            target=forward,
+            name=f"{host}:{port}<->{target_host}:{target_port}",
+            args=(host, port, target_host, target_port, terminate_event),
+            daemon=True,
+        )
+        thread.start()
+        self.forwarders[(host, port)] = (terminate_event, thread)
+
+    def stop(self, host, port):
+        """Stop the forwarding worker listening on the given host and port
+
+        Args:
+            host (str): hostname or ip address the worker is listening
+                on
+            port (str): port number the worker is listening on
+
+        """
+        terminate_event, thread = self.forwarders.pop((host, port))
+        terminate_event.set()
+        thread.join(timeout=5)
+
+    def stop_all(self):
+        """
+        Stop all the forwarding workers
+        """
+        threads = []
+        for (host, port) in self.forwarders:
+            terminate_event, thread = self.forwarders[(host, port)]
+            terminate_event.set()
+            threads.append(thread)
+        for thread in threads:
+            thread.join(timeout=1)
+            # FIXME: sometimes some threads don't terminate in CI, so
+            # we're dumping the stacktraces for all the current
+            # threads for debugging purpose.
+            if thread.isAlive():
+                faulthandler.dump_traceback()
+                raise Exception(f"Thread {thread.name} is still alive")
+        self.forwarders = {}
diff --git a/tests/store.py b/tests/store.py
new file mode 100644
index 000000000..9534ccb24
--- /dev/null
+++ b/tests/store.py
@@ -0,0 +1,101 @@
+"""This module provide a subclass of
+`xain_fl.coordinator.store.Store` that stores data in memory.
+"""
+from collections import defaultdict
+import pickle
+import typing
+
+import numpy as np
+
+from xain_fl.coordinator.store import Store, StoreConfig
+
+
+class FakeS3Resource:
+    """Mock of the `xain-fl.coordinator.Store.s3` attribute.
+
+    This class offers the same API than `boto3.S3.Client.bucket` but
+    writes data in memory and keeps track of the reads and writes.
+    """
+
+    def __init__(self):
+        # fake store where data gets written to
+        self.fake_store = {}
+        # count the writes for each key in the store
+        self.writes = defaultdict(lambda: 0)
+        # count the reads for each key in the store
+        self.reads = defaultdict(lambda: 0)
+
+    # The names come from the `boto3` API we're mocking
+    # pylint: disable=invalid-name
+    def Bucket(self, _name: str):
+        """Mock of the `boto3.S3.Client.Bucket` method.
+
+        Args:
+            _name (str): name of the bucket (un-used)
+        """
+        return self
+
+    # The names come from the `boto3` API we're mocking
+    # pylint: disable=invalid-name
+    def put_object(self, Body: bytes, Key: str):
+        """Mock of the `boto3.S3.Client.put_object` method.
+
+        Args:
+            Body (bytes): data to write to the bucket
+            Key (str): key under which the data should be stored
+        """
+        # We store the data non-serialized, to make it easier to
+        # check it.
+        self.fake_store[Key] = pickle.loads(Body)
+        self.writes[Key] += 1
+
+    def download_fileobj(self, key: str, buf: typing.IO):
+        """Mock of the `boto3.S3.Client.download_fileobj` method.
+
+        Args:
+            key (str): key under which the data to retrieve is stored
+            buf (bytes of file-like object): buffer for writing the data
+        """
+        data = pickle.dumps(self.fake_store[key])
+        buf.write(data)
+        self.reads[key] += 1
+
+
+class TestStore(Store):
+    """A partial mock of the `xain-fl.coordinator.store.Store` class that
+    does not perform any IO. Instead, data is stored in memory.
+    """
+
+    # We DO NOT want to call the parent class __init__, since it tries
+    # to initialize a connection to a non-existent external resource
+    #
+    # pylint: disable=super-init-not-called
+    def __init__(self):
+        self.config = StoreConfig("endpoint_url", "access_key_id", "secret_access_key", "bucket")
+        self.s3 = FakeS3Resource()
+
+    def assert_wrote(self, round: int, weights: np.ndarray):
+        """Check that the given weights have been written to the store for the
+given round.
+
+        Args:
+            weights (np.ndarray): weights to store
+            round (int): round to which the weights belong
+        """
+        writes = self.s3.writes[str(round)]
+        # Under normal conditions, we should write data exactly once
+        assert writes == 1, f"got {writes} writes for round {round}, expected 1"
+        # If the arrays contains `NaN` we cannot compare them, so we
+        # replace them by zeros to do the comparison
+        stored_array = np.nan_to_num(self.s3.fake_store[str(round)])
+        expected_array = np.nan_to_num(weights)
+        assert np.array_equal(stored_array, expected_array)
+
+    def assert_didnt_write(self, round: int):
+        """Check that the weights for the given round have NOT been written to the store.
+
+        Args:
+            round (int): round to which the weights belong
+
+        """
+        assert self.s3.writes[str(round)] == 0
diff --git a/tests/test_coordinator.py b/tests/test_coordinator.py
new file mode 100644
index 000000000..9add70f07
--- /dev/null
+++ b/tests/test_coordinator.py
@@ -0,0 +1,321 @@
+"""XAIN FL tests for coordinator"""
+
+import numpy as np
+import pytest
+from xain_proto.fl.coordinator_pb2 import (
+    EndTrainingRoundRequest,
+    HeartbeatRequest,
+    RendezvousReply,
+    RendezvousRequest,
+    RendezvousResponse,
+    StartTrainingRoundRequest,
+    State,
+)
+from xain_proto.numproto import proto_to_ndarray
+
+from xain_fl.coordinator.coordinator import Coordinator
+from xain_fl.tools.exceptions import (
+    DuplicatedUpdateError,
+    InvalidRequestError,
+    UnknownParticipantError,
+)
+
+
+def test_rendezvous_accept():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator: Coordinator = Coordinator()
+    response: RendezvousResponse = coordinator.on_message(RendezvousRequest(), "participant1")
+
+    assert isinstance(response, RendezvousResponse)
+    assert response.reply == RendezvousReply.ACCEPT
+
+
+def test_rendezvous_later_fraction_1():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    response = coordinator.on_message(RendezvousRequest(), "participant2")
+
+    assert isinstance(response, RendezvousResponse)
+    assert response.reply == RendezvousReply.LATER
+
+
+def test_rendezvous_later_fraction_05():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=0.5)
+
+    # with 0.5 fraction it needs to accept at least two participants
+    response = coordinator.on_message(RendezvousRequest(), "participant1")
+
+    assert isinstance(response, RendezvousResponse)
+    assert response.reply == RendezvousReply.ACCEPT
+
+    response = coordinator.on_message(RendezvousRequest(), "participant2")
+
+    assert isinstance(response, RendezvousResponse)
+    assert response.reply == RendezvousReply.ACCEPT
+
+    # the third participant must receive LATER RendezvousReply
+    response = coordinator.on_message(RendezvousRequest(), "participant3")
+
+    assert isinstance(response, RendezvousResponse)
+    assert response.reply == RendezvousReply.LATER
+
+
+def test_coordinator_state_standby_round():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # tests that the coordinator transitions from STANDBY to ROUND once enough participants
+    # are connected
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=1.0)
+
+    assert coordinator.state == State.STANDBY
+
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    assert coordinator.state == State.ROUND
+    assert coordinator.current_round == 1
+
+
+def test_start_training_round():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    test_weights = [np.arange(10), np.arange(10, 20)]
+    coordinator = Coordinator(
+        minimum_participants_in_round=1, fraction_of_participants=1.0, weights=test_weights,
+    )
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    response = coordinator.on_message(StartTrainingRoundRequest(), "participant1")
+    received_weights = [proto_to_ndarray(nda) for nda in response.weights]
+
+    np.testing.assert_equal(test_weights, received_weights)
+
+
+def start_training_round_wrong_state():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # if the coordinator receives a StartTrainingRound request while not in the
+    # ROUND state it will raise an exception
+    coordinator = Coordinator(minimum_participants_in_round=2, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    with pytest.raises(InvalidRequestError):
+        coordinator.on_message(StartTrainingRoundRequest(), "participant1")
+
+
+def test_end_training_round():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # we need two participants so that we can check the status of the local update mid round
+    # with only one participant it wouldn't work because the local updates state is cleaned at
+    # the end of each round
+    coordinator = Coordinator(minimum_participants_in_round=2, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+
+    assert len(coordinator.round.updates) == 1
+
+
+def test_end_training_round_update():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # Test that the round number is updated once all participants sent their updates
+    coordinator = Coordinator(
+        minimum_participants_in_round=2, fraction_of_participants=1.0, num_rounds=2
+    )
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    # check that we are currently in round 1
+    assert coordinator.current_round == 1
+
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+    # check we are still in round 1
+    assert coordinator.current_round == 1
+    coordinator.on_message(EndTrainingRoundRequest(), "participant2")
+
+    # check that round number was updated
+    assert coordinator.current_round == 2
+
+
+def test_end_training_round_reinitialize_local_models():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator = Coordinator(
+        minimum_participants_in_round=2, fraction_of_participants=1.0, num_rounds=2
+    )
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+
+    # After one participant sends its updates we should have one update in the coordinator
+    assert len(coordinator.round.updates) == 1
+
+    coordinator.on_message(EndTrainingRoundRequest(), "participant2")
+
+    # once the second participant delivers its updates the round ends and the local models
+    # are reinitialized
+    assert coordinator.round.updates == {}
+
+
+def test_training_finished():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator = Coordinator(
+        minimum_participants_in_round=1, fraction_of_participants=1.0, num_rounds=2
+    )
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    # Deliver results for 2 rounds
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+
+    assert coordinator.state == State.FINISHED
+
+
+def test_wrong_participant():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # coordinator should not accept requests from participants that it has not accepted
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    with pytest.raises(UnknownParticipantError):
+        coordinator.on_message(HeartbeatRequest(), "participant2")
+
+    with pytest.raises(UnknownParticipantError):
+        coordinator.on_message(StartTrainingRoundRequest(), "participant2")
+
+    with pytest.raises(UnknownParticipantError):
+        coordinator.on_message(EndTrainingRoundRequest(), "participant2")
+
+
+def test_duplicated_update_submit():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # the coordinator should not accept multiples updates from the same participant
+    # in the same round
+    coordinator = Coordinator(minimum_participants_in_round=2, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+
+    with pytest.raises(DuplicatedUpdateError):
+        coordinator.on_message(EndTrainingRoundRequest(), "participant1")
+
+
+def test_remove_participant():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=1.0)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    assert coordinator.state == State.ROUND
+
+    coordinator.remove_participant("participant1")
+
+    assert coordinator.participants.len() == 0
+    assert coordinator.state == State.STANDBY
+
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    assert coordinator.state == State.ROUND
+
+
+def test_number_of_selected_participants():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # test that the coordinator needs minimum 3 participants and selects 2 of them
+    coordinator = Coordinator(minimum_participants_in_round=2, fraction_of_participants=0.6)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+
+    # the coordinator should wait for three participants to be connected before starting a round,
+    # and select participants. Before that coordinator.round.participant_ids is an empty list
+    assert coordinator.minimum_connected_participants == 3
+    assert coordinator.state == State.STANDBY
+    assert coordinator.round.participant_ids == []
+
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    assert coordinator.state == State.STANDBY
+    assert coordinator.round.participant_ids == []
+
+    # add the third participant
+    coordinator.on_message(RendezvousRequest(), "participant3")
+
+    # now the coordinator must have started a round and selected 2 participants
+    assert coordinator.state == State.ROUND
+    assert len(coordinator.round.participant_ids) == 2
+
+
+def test_correct_round_advertised_to_participants():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    # test that only selected participants receive ROUND state and the others STANDBY
+    coordinator = Coordinator(minimum_participants_in_round=1, fraction_of_participants=0.5)
+    coordinator.on_message(RendezvousRequest(), "participant1")
+    coordinator.on_message(RendezvousRequest(), "participant2")
+
+    # override selected participant
+    coordinator.round.participant_ids = ["participant1"]
+
+    # state ROUND will be advertised to participant1 (which has been selected)
+    response = coordinator.on_message(HeartbeatRequest(), "participant1")
+    assert response.state == State.ROUND
+
+    # state STANDBY will be advertised to participant2 (which has NOT been selected)
+    response = coordinator.on_message(HeartbeatRequest(), "participant2")
+    assert response.state == State.STANDBY
diff --git a/tests/test_grpc.py b/tests/test_grpc.py
new file mode 100644
index 000000000..8e36ecfcf
--- /dev/null
+++ b/tests/test_grpc.py
@@ -0,0 +1,484 @@
+"""XAIN FL tests for gRPC coordinator"""
+
+from concurrent import futures
+import threading
+from unittest import mock
+
+import grpc
+import numpy as np
+import pytest
+from xain_proto.fl.coordinator_pb2 import (
+    EndTrainingRoundRequest,
+    EndTrainingRoundResponse,
+    HeartbeatRequest,
+    HeartbeatResponse,
+    RendezvousReply,
+    RendezvousRequest,
+    StartTrainingRoundRequest,
+    StartTrainingRoundResponse,
+    State,
+)
+from xain_proto.fl.coordinator_pb2_grpc import add_CoordinatorServicer_to_server
+from xain_proto.fl.hellonumproto_pb2 import NumProtoRequest
+from xain_proto.fl.hellonumproto_pb2_grpc import NumProtoServerStub
+from xain_proto.numproto import ndarray_to_proto, proto_to_ndarray
+from xain_sdk.participant_state_machine import (
+    StateRecord,
+    end_training_round,
+    message_loop,
+    rendezvous,
+    start_participant,
+    start_training_round,
+)
+
+from xain_fl.coordinator.coordinator import Coordinator
+from xain_fl.coordinator.coordinator_grpc import CoordinatorGrpc
+from xain_fl.coordinator.heartbeat import monitor_heartbeats
+from xain_fl.coordinator.participants import Participants
+
+from .store import TestStore
+
+
+@pytest.mark.integration
+def test_greeter_server(greeter_server):  # pylint: disable=unused-argument
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        greeter_server ([type]): [description]
+    """
+
+    with grpc.insecure_channel("localhost:50051") as channel:
+        stub = NumProtoServerStub(channel)
+
+        nda = np.arange(10)
+        response = stub.SayHelloNumProto(NumProtoRequest(arr=ndarray_to_proto(nda)))
+
+        response_nda = proto_to_ndarray(response.arr)
+
+        assert np.array_equal(nda * 2, response_nda)
+
+
+@pytest.mark.integration
+def test_participant_rendezvous_accept(  # pylint: disable=unused-argument
+    participant_stub, coordinator_service
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    response = participant_stub.Rendezvous(RendezvousRequest())
+
+    assert response.reply == RendezvousReply.ACCEPT
+
+
+@pytest.mark.integration
+def test_participant_rendezvous_later(participant_stub):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+    """
+
+    # populate participants
+    coordinator = Coordinator(minimum_participants_in_round=10, fraction_of_participants=1.0)
+    required_participants = 10
+    for i in range(required_participants):
+        coordinator.participants.add(str(i))
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
+    store = TestStore()
+    add_CoordinatorServicer_to_server(CoordinatorGrpc(coordinator, store), server)
+    server.add_insecure_port("localhost:50051")
+    server.start()
+
+    # try to rendezvous the 11th participant
+    response = participant_stub.Rendezvous(RendezvousRequest())
+    server.stop(0)
+
+    assert response.reply == RendezvousReply.LATER
+
+
+@pytest.mark.integration
+def test_heartbeat(participant_stub, coordinator_service):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    # first we need to rendezvous so that the participant is added to the list of participants
+    _ = participant_stub.Rendezvous(RendezvousRequest())
+    response = participant_stub.Heartbeat(HeartbeatRequest())
+
+    # the Coordinator is initialised in conftest.py::coordinator_service with 10 participants
+    # needed per round. so here we expect the HeartbeatResponse to have State.STANDBY
+    # because we connected only one participant
+    assert response == HeartbeatResponse()
+    assert coordinator_service.coordinator.state == State.STANDBY
+
+
+@pytest.mark.integration
+def test_heartbeat_denied(participant_stub, coordinator_service):  # pylint: disable=unused-argument
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    # heartbeat requests are only allowed if the participant has already
+    # rendezvous with the coordinator
+    with pytest.raises(grpc.RpcError):
+        response = participant_stub.Heartbeat(HeartbeatRequest())
+        assert response.status_code == grpc.StatusCode.PERMISSION_DENIED
+
+
+@mock.patch("xain_fl.coordinator.heartbeat.threading.Event.is_set", side_effect=[False, True])
+@mock.patch("xain_fl.coordinator.heartbeat.time.sleep", return_value=None)
+@mock.patch("xain_fl.coordinator.heartbeat.Coordinator.remove_participant")
+def test_monitor_heartbeats(mock_participants_remove, _mock_sleep, _mock_event):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        mock_participants_remove ([type]): [description]
+        _mock_sleep ([type]): [description]
+        _mock_event ([type]): [description]
+    """
+
+    participants = Participants()
+    participants.add("participant_1")
+    participants.participants["participant_1"].heartbeat_expires = 0
+
+    coordinator = Coordinator()
+    coordinator.participants = participants
+
+    terminate_event = threading.Event()
+    monitor_heartbeats(coordinator, terminate_event)
+
+    mock_participants_remove.assert_called_once_with("participant_1")
+
+
+@mock.patch("xain_fl.coordinator.heartbeat.threading.Event.is_set", side_effect=[False, True])
+@mock.patch("xain_fl.coordinator.heartbeat.time.sleep", return_value=None)
+def test_monitor_heartbeats_remove_participant(_mock_sleep, _mock_event):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        _mock_sleep ([type]): [description]
+        _mock_event ([type]): [description]
+    """
+
+    participants = Participants()
+    participants.add("participant_1")
+    participants.participants["participant_1"].heartbeat_expires = 0
+
+    coordinator = Coordinator()
+    coordinator.participants = participants
+
+    terminate_event = threading.Event()
+    monitor_heartbeats(coordinator, terminate_event)
+
+    assert participants.len() == 0
+
+
+@mock.patch(
+    "xain_sdk.participant_state_machine.threading.Event.is_set", side_effect=[False, False, True]
+)
+@mock.patch("xain_sdk.participant_state_machine.time.sleep", return_value=None)
+@mock.patch("xain_sdk.participant_state_machine.HeartbeatRequest")
+def test_message_loop(mock_heartbeat_request, _mock_sleep, _mock_event):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        mock_heartbeat_request ([type]): [description]
+        _mock_sleep ([type]): [description]
+        _mock_event ([type]): [description]
+    """
+
+    channel = mock.MagicMock()
+    terminate_event = threading.Event()
+    state_record = StateRecord()
+
+    message_loop(channel, state_record, terminate_event)
+
+    # check that the heartbeat is sent exactly twice
+    mock_heartbeat_request.assert_has_calls([mock.call(), mock.call()])
+
+
+@pytest.mark.integration
+def test_start_training_round(coordinator_service):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        coordinator_service ([type]): [description]
+    """
+
+    test_weights = [np.arange(10), np.arange(10, 20)]
+
+    # set coordinator global model and hyper-params so that it needs only 1 participant
+    coord = coordinator_service.coordinator
+    coord.minimum_participants_in_round = 1
+    coord.fraction_of_participants = 1.0
+    coord.epochs = 5
+    coord.epoch_base = 2
+    coord.weights = test_weights
+    coord.minimum_connected_participants = coord.get_minimum_connected_participants()
+
+    # simulate a participant communicating with coordinator via channel
+    with grpc.insecure_channel("localhost:50051") as channel:
+        # we need to rendezvous before we can send any other requests
+        rendezvous(channel)
+        # call StartTrainingRound service method on coordinator
+        weights, epochs, epoch_base = start_training_round(channel)
+
+    # check global model received
+    assert epochs == 5
+    assert epoch_base == 2
+    np.testing.assert_equal(weights, test_weights)
+
+
+@pytest.mark.integration
+def test_start_training_round_denied(  # pylint: disable=unused-argument
+    participant_stub, coordinator_service
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    # start training requests are only allowed if the participant has already
+    # rendezvous with the coordinator
+    with pytest.raises(grpc.RpcError):
+        response = participant_stub.StartTrainingRound(StartTrainingRoundRequest())
+        assert response.status_code == grpc.StatusCode.PERMISSION_DENIED
+
+
+@pytest.mark.integration
+def test_start_training_round_failed_precondition(  # pylint: disable=unused-argument
+    participant_stub, coordinator_service
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    # start training requests are only allowed if the coordinator is in ROUND state.
+    # Since we need 10 participants to be connected (see conftest.py::coordinator_service)
+    # the StartTrainingRoundRequest is expected to fail
+    participant_stub.Rendezvous(RendezvousRequest())
+    with pytest.raises(grpc.RpcError):
+        response = participant_stub.StartTrainingRound(StartTrainingRoundRequest())
+        assert response.status_code == grpc.StatusCode.FAILED_PRECONDITION
+
+
+@pytest.mark.integration
+def test_end_training_round(coordinator_service):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        coordinator_service ([type]): [description]
+    """
+
+    assert coordinator_service.coordinator.round.updates == {}
+
+    # simulate trained local model data
+    test_weights, number_samples = [np.arange(20, 30), np.arange(30, 40)], 2
+    test_metrics = {"metric": np.arange(10, 20)}
+
+    with grpc.insecure_channel("localhost:50051") as channel:
+        # we first need to rendezvous before we can send any other request
+        rendezvous(channel)
+        # call EndTrainingRound service method on coordinator
+        end_training_round(channel, test_weights, number_samples, test_metrics)
+    # check local model received...
+
+    assert len(coordinator_service.coordinator.round.updates) == 1
+
+    round_ = coordinator_service.coordinator.round
+
+    # first the weights update
+    _, round_update = round_.updates.popitem()
+    update_item1, update_item2 = round_update["weight_update"]
+    assert update_item2 == number_samples
+    np.testing.assert_equal(update_item1, test_weights)
+
+    round_update_metrics = round_update["metrics"]
+    assert round_update_metrics.keys() == test_metrics.keys()
+    for key, values in test_metrics.items():
+        np.testing.assert_equal(round_update_metrics[key], values)
+
+
+@pytest.mark.integration
+def test_end_training_round_duplicated_updates(  # pylint: disable=unused-argument
+    coordinator_service, participant_stub
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        coordinator_service ([type]): [description]
+        participant_stub ([type]): [description]
+    """
+
+    # participant can only send updates once in a single round
+    participant_stub.Rendezvous(RendezvousRequest())
+
+    participant_stub.EndTrainingRound(EndTrainingRoundRequest())
+
+    with pytest.raises(grpc.RpcError):
+        response = participant_stub.EndTrainingRound(EndTrainingRoundRequest())
+        assert response.status_code == grpc.StatusCode.ALREADY_EXISTS
+
+
+@pytest.mark.integration
+def test_end_training_round_denied(  # pylint: disable=unused-argument
+    participant_stub, coordinator_service
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        participant_stub ([type]): [description]
+        coordinator_service ([type]): [description]
+    """
+
+    # heartbeat requests are only allowed if the participant has already
+    # rendezvous with the coordinator
+    with pytest.raises(grpc.RpcError):
+        response = participant_stub.EndTrainingRound(EndTrainingRoundRequest())
+        assert response.status_code == grpc.StatusCode.PERMISSION_DENIED
+
+
+@pytest.mark.integration
+def test_full_training_round(
+    participant_stubs, coordinator_service
+):  # pylint: disable=unused-argument
+    """Run a complete training round with multiple participants.
+    """
+    # Initialize the coordinator with dummy weights, otherwise, the
+    # aggregated weights at the end of the round are an empty array.
+    dummy_weights = np.ndarray([1, 2, 3, 4])
+    coordinator_service.coordinator.weights = dummy_weights
+    weights_proto = [ndarray_to_proto(nda) for nda in dummy_weights]
+
+    # Create 10 partipants
+    participants = [next(participant_stubs) for _ in range(0, 10)]
+
+    # 9 participants out of 10 connect to the coordinator, so it stays
+    # in STANDBY and accepts the connections.
+    for participant in participants[:-1]:
+        response = participant.Rendezvous(RendezvousRequest())
+        assert response.reply == RendezvousReply.ACCEPT
+
+        response = participant.Heartbeat(HeartbeatRequest())
+        assert response == HeartbeatResponse(state=State.STANDBY, round=0)
+
+    assert coordinator_service.coordinator.state == State.STANDBY
+    assert coordinator_service.coordinator.current_round == 0
+
+    # The 10th participant connect, so the coordinator switches to ROUND>
+    last_participant = participants[-1]
+    response = last_participant.Rendezvous(RendezvousRequest())
+    assert response.reply == RendezvousReply.ACCEPT
+
+    assert coordinator_service.coordinator.state == State.ROUND
+    assert coordinator_service.coordinator.current_round == 1
+
+    response = last_participant.Heartbeat(HeartbeatRequest())
+    assert response == HeartbeatResponse(state=State.ROUND, round=1)
+
+    # The initial 9 participants send another heatbeat request.
+    for participant in participants[:-1]:
+        response = participant.Heartbeat(HeartbeatRequest(state=State.STANDBY, round=0))
+    assert response == HeartbeatResponse(state=State.ROUND, round=1)
+
+    # The participants start training
+    for participant in participants:
+        response = participant.StartTrainingRound(StartTrainingRoundRequest())
+        assert response == StartTrainingRoundResponse(
+            weights=weights_proto,
+            epochs=coordinator_service.coordinator.epochs,
+            epoch_base=coordinator_service.coordinator.epoch_base,
+        )
+
+    # The first 9th participants end training
+    for participant in participants[:-1]:
+        response = participant.EndTrainingRound(
+            EndTrainingRoundRequest(weights=weights_proto, number_samples=1)
+        )
+        assert response == EndTrainingRoundResponse()
+
+    assert not coordinator_service.coordinator.round.is_finished()
+    coordinator_service.store.assert_didnt_write(1)
+
+    # The last participant finishes training
+    response = last_participant.EndTrainingRound(EndTrainingRoundRequest())
+    assert response == EndTrainingRoundResponse()
+    # Make sure we wrote the results for the given round
+    coordinator_service.store.assert_wrote(1, coordinator_service.coordinator.weights)
+
+
+@pytest.mark.integration
+@pytest.mark.slow
+def test_start_participant(mock_coordinator_service):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        mock_coordinator_service ([type]): [description]
+    """
+
+    init_weight = [np.arange(10), np.arange(10, 20)]
+    mock_coordinator_service.coordinator.weights = init_weight
+
+    # mock a local participant with a constant train_round function
+    with mock.patch("xain_sdk.participant_state_machine.Participant") as mock_obj:
+        mock_local_part = mock_obj.return_value
+        mock_local_part.train_round.return_value = init_weight, 1, {}
+
+        start_participant(mock_local_part, "localhost:50051")
+
+        coord = mock_coordinator_service.coordinator
+        assert coord.state == State.FINISHED
+
+        # coordinator set to 2 round for good measure, but the resulting
+        # aggregated weights are the same as a single round
+        assert coord.current_round == 2
+
+        # expect weight aggregated by summation - see mock_coordinator_service
+        np.testing.assert_equal(coord.weights, [np.arange(start=10, stop=29, step=2)])
diff --git a/xain_fl/CONFIG.md b/xain_fl/CONFIG.md
deleted file mode 100644
index 468ad3279..000000000
--- a/xain_fl/CONFIG.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Configuration
-
-Currently configuration happens two fold.
-
-## `config.cfg`
-
-On the initial run of the library the `xain_fl.config.init_config()` function
-will be executed and initialize a `config.cfg` file in the projects root directory.
-If the user wants to run benchmarks and wants results to be uploaded to S3 he will need to replace the
-`ACCESSIBLE_S3_BUCKET_FOR_RESULTS_TO_BE_UPLOADED` string in the `config.cfg` with an actual S3 bucket name. The bucket
-should be accessible with the currently active AWS credentials (either default credentials or the currently set AWS_PROFILE).
-
-## abseil.io flags
-
-The second form of configuration happens via abseil.io flags. All entry points are configured as abseil.io apps which
-accept various flags. To find out what valid flag exist one can run any entry point with the `--helpfull` flag.
-For example
-
-```shell
-$ aggregate --helpfull
-```
diff --git a/xain_fl/__init__.py b/xain_fl/__init__.py
index e762f6393..0ba763dba 100644
--- a/xain_fl/__init__.py
+++ b/xain_fl/__init__.py
@@ -1,31 +1 @@
-import os
-
-from absl import flags
-
-import xain_fl.config
-
-if not xain_fl.config.check_config_file_exists():
-    xain_fl.config.init_config()
-
-c = xain_fl.config.load()
-
-# following: https://abseil.io/docs/cpp/guides/flags#flags-best-practices
-# we will define our flags in this file
-FLAGS = flags.FLAGS
-
-
-flags.DEFINE_string(
-    "output_dir", c.get("Path", "output_dir"), "Output directory as absolute path"
-)
-
-flags.DEFINE_string(
-    "results_dir", c.get("Path", "results_dir"), "Results directory as absolute path"
-)
-
-flags.DEFINE_string(
-    "S3_results_bucket",
-    c.get("S3", "results_bucket", fallback=None),
-    "Bucket name for the results to be uploaded to",
-)
-
-os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+"""XAIN FL init"""
diff --git a/xain_fl/__version__.py b/xain_fl/__version__.py
index d762ba20c..214b32310 100644
--- a/xain_fl/__version__.py
+++ b/xain_fl/__version__.py
@@ -1,4 +1,4 @@
 """central place to handle versioning"""
-__version__ = "0.2.0"
+__version__ = "0.3.0"
 # used by the documentation
-__short_version__ = "0.2"
+__short_version__ = "0.3"
diff --git a/xain_fl/cli.py b/xain_fl/cli.py
index c403d2a82..c04f2698e 100644
--- a/xain_fl/cli.py
+++ b/xain_fl/cli.py
@@ -8,38 +8,80 @@
 import numpy as np
 
 from xain_fl.coordinator.coordinator import Coordinator
+from xain_fl.coordinator.store import Store, StoreConfig
 from xain_fl.serve import serve
 
 
 def type_num_rounds(value):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        value ([type]): [description]
+
+    Returns:
+        [type]: [description]
+
+    Raises:
+        ~argparse.ArgumentTypeError: [description]
+        ~argparse.ArgumentTypeError: [description]
+    """
+
     ivalue = int(value)
 
     if ivalue <= 0:
         raise argparse.ArgumentTypeError("%s is an invalid positive int value" % value)
 
     if ivalue > 1_000:
-        raise argparse.ArgumentTypeError(
-            "%s More than 1_000 rounds is not supported" % value
-        )
+        raise argparse.ArgumentTypeError("%s More than 1_000 rounds is not supported" % value)
 
     return ivalue
 
 
 def type_num_epochs(value):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        value ([type]): [description]
+
+    Returns:
+        [type]: [description]
+
+    Raises:
+        ~argparse.ArgumentTypeError: [description]
+        ~argparse.ArgumentTypeError: [description]
+    """
+
     ivalue = int(value)
 
-    if ivalue <= 0:
+    if ivalue < 0:
         raise argparse.ArgumentTypeError("%s is an invalid positive int value" % value)
 
     if ivalue > 10_000:
-        raise argparse.ArgumentTypeError(
-            "%s More than 10_000 epochs is not supported" % value
-        )
+        raise argparse.ArgumentTypeError("%s More than 10_000 epochs is not supported" % value)
 
     return ivalue
 
 
 def type_min_num_participants_in_round(value):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        value ([type]): [description]
+
+    Returns:
+        [type]: [description]
+
+    Raises:
+        ~argparse.ArgumentTypeError: [description]
+        ~argparse.ArgumentTypeError: [description]
+    """
+
     ivalue = int(value)
 
     if ivalue <= 0:
@@ -54,12 +96,25 @@ def type_min_num_participants_in_round(value):
 
 
 def type_fraction(value):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        value ([type]): [description]
+
+    Returns:
+        [type]: [description]
+
+    Raises:
+        ~argparse.ArgumentTypeError: [description]
+        ~argparse.ArgumentTypeError: [description]
+    """
+
     ivalue = float(value)
 
     if ivalue <= 0:
-        raise argparse.ArgumentTypeError(
-            "%s is an invalid positive float value" % value
-        )
+        raise argparse.ArgumentTypeError("%s is an invalid positive float value" % value)
 
     if ivalue > 1:
         raise argparse.ArgumentTypeError(
@@ -70,6 +125,14 @@ def type_fraction(value):
 
 
 def get_cmd_parameters():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Returns:
+        [type]: [description]
+    """
+
     # Allow various parameters to be passed via the commandline
     parser = argparse.ArgumentParser(description="Coordinator CLI")
 
@@ -116,10 +179,39 @@ def get_cmd_parameters():
             A float between 0 and 1",
     )
 
+    parser.add_argument(
+        "--storage-endpoint", required=True, type=str, help="URL to the storage service to use",
+    )
+
+    parser.add_argument(
+        "--storage-bucket",
+        required=True,
+        type=str,
+        help="Name of the bucket for storing the aggregated models",
+    )
+
+    parser.add_argument(
+        "--storage-key-id",
+        required=True,
+        type=str,
+        help="AWS access key ID to use to authenticate to the storage service",
+    )
+
+    parser.add_argument(
+        "--storage-secret-access-key",
+        required=True,
+        type=str,
+        help="AWS secret access to use to authenticate to the storage service",
+    )
     return parser.parse_args()
 
 
 def main():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
     parameters = get_cmd_parameters()
 
     coordinator = Coordinator(
@@ -130,7 +222,15 @@ def main():
         fraction_of_participants=parameters.fraction,
     )
 
-    serve(coordinator=coordinator, host=parameters.host, port=parameters.port)
+    store_config = StoreConfig(
+        parameters.storage_endpoint,
+        parameters.storage_key_id,
+        parameters.storage_secret_access_key,
+        parameters.storage_bucket,
+    )
+    store = Store(store_config)
+
+    serve(coordinator=coordinator, store=store, host=parameters.host, port=parameters.port)
 
 
 if __name__ == "__main__":
diff --git a/xain_fl/config.py b/xain_fl/config.py
deleted file mode 100644
index cccabbb0f..000000000
--- a/xain_fl/config.py
+++ /dev/null
@@ -1,68 +0,0 @@
-import configparser
-import os
-from functools import lru_cache
-from pathlib import Path
-
-from xain_fl.helpers import project
-from xain_fl.logger import get_logger
-
-# Storage dir for bigger files like the datasets
-storage_dir = Path.home().joinpath(".xain-fl")
-datasets_dir_default = storage_dir.joinpath("datasets")
-
-# Local outputs and remote results
-root_dir = project.root()
-config_file = root_dir.joinpath("config.cfg")
-output_dir_default = root_dir.joinpath("output")
-results_dir_default = root_dir.joinpath("results")
-
-logger = get_logger(__name__)
-
-
-def init_config():
-    """Creates initial config file if non exists"""
-    logger.info("Initializing config", config_file=config_file)
-
-    config = configparser.ConfigParser()
-
-    # Path config section
-    config.add_section("Path")
-    config.set("Path", "output_dir", str(output_dir_default))
-    config.set("Path", "results_dir", str(results_dir_default))
-    config.set("Path", "local_datasets_dir", str(datasets_dir_default))
-
-    config.add_section("S3")
-    config.set(
-        "S3",
-        "results_bucket",
-        # Using an ENV variable here to we can set it on the CI as an environment variable
-        os.environ.get(
-            "S3_RESULTS_BUCKET",
-            default="ACCESSIBLE_S3_BUCKET_FOR_RESULTS_TO_BE_UPLOADED",
-        ),
-    )
-
-    # Dataset config section
-    config.add_section("Dataset")
-    config.set(
-        "Dataset",
-        "repository",
-        "http://xain-datasets.s3-website.eu-central-1.amazonaws.com",
-    )
-    config.set(
-        "Dataset", "fetch_datasets", "True"
-    )  # Indicates if datasets should be fetched from remote by default
-
-    with open(config_file, "w") as configfile:
-        config.write(configfile)
-
-
-def check_config_file_exists():
-    return os.path.isfile(config_file)
-
-
-@lru_cache()  # Using this to avoid loading file every time from disk
-def load():
-    config = configparser.ConfigParser()
-    config.read(config_file)
-    return config
diff --git a/xain_fl/conftest.py b/xain_fl/conftest.py
deleted file mode 100644
index fb4cda4d9..000000000
--- a/xain_fl/conftest.py
+++ /dev/null
@@ -1,88 +0,0 @@
-import numpy as np
-import pytest
-from absl import flags
-
-from xain_fl.types import FederatedDataset, KerasDataset
-
-FLAGS = flags.FLAGS
-
-
-def pytest_collection_modifyitems(items):
-    for item in items:
-        if not any(item.iter_markers()):
-            item.add_marker("unmarked")
-
-
-def pytest_runtest_setup():
-    # Invoking FLAGS will make the flags usable for the
-    # test execution and avoid throwing an error
-    FLAGS(
-        argv=[
-            "test",  # some app name required
-            "--fetch_datasets=True",  # resetting to default at beginning of every test
-        ]
-    )
-
-
-@pytest.fixture
-def output_dir(tmpdir):
-    tmpdir = str(tmpdir)
-    FLAGS(["test", f"--output_dir={tmpdir}"])
-
-    return tmpdir
-
-
-@pytest.fixture
-def disable_fetch():
-    FLAGS(["test", "--fetch_datasets=False"])
-
-
-def create_mock_keras_dataset() -> KerasDataset:
-    labels = np.arange(10, dtype=np.int8)
-
-    x_train = np.ones((600, 32, 32, 3), dtype=np.int8)
-    y_train = np.tile(labels, 60)
-    assert x_train.shape[0] == y_train.shape[0]
-
-    x_test = np.ones((100, 32, 32, 3), dtype=np.int8)
-    y_test = np.tile(labels, 10)
-    assert x_test.shape[0] == y_test.shape[0]
-
-    return (x_train, y_train), (x_test, y_test)
-
-
-def create_mock_federated_dataset() -> FederatedDataset:
-    labels = np.arange(10, dtype=np.int8)
-
-    x_train = np.ones((540, 32, 32, 3), dtype=np.int8)
-    y_train = np.tile(labels, 54)
-    assert x_train.shape[0] == y_train.shape[0]
-
-    xy_partitions = list(
-        zip(
-            np.split(x_train, indices_or_sections=2, axis=0),
-            np.split(y_train, indices_or_sections=2, axis=0),
-        )
-    )
-
-    x_val = np.ones((60, 32, 32, 3), dtype=np.int8)
-    y_val = np.tile(labels, 6)
-    assert x_val.shape[0] == y_val.shape[0]
-
-    x_test = np.ones((100, 32, 32, 3), dtype=np.int8)
-    y_test = np.tile(labels, 10)
-    assert x_test.shape[0] == y_test.shape[0]
-
-    return xy_partitions, (x_val, y_val), (x_test, y_test)
-
-
-@pytest.fixture
-def mock_keras_dataset() -> KerasDataset:
-    """dataset mock after it went through internal load method"""
-    return create_mock_keras_dataset()
-
-
-@pytest.fixture
-def mock_federated_dataset() -> FederatedDataset:
-    """dataset mock after it went through internal load method"""
-    return create_mock_federated_dataset()
diff --git a/xain_fl/coordinator/coordinator.py b/xain_fl/coordinator/coordinator.py
index 45d3fb7d8..5ed828ecf 100644
--- a/xain_fl/coordinator/coordinator.py
+++ b/xain_fl/coordinator/coordinator.py
@@ -1,89 +1,115 @@
+"""XAIN FL Coordinator"""
+
 from typing import Dict, List, Optional, Tuple
 
 from google.protobuf.internal.python_message import GeneratedProtocolMessageType
-from numproto import ndarray_to_proto, proto_to_ndarray
 from numpy import ndarray
+from xain_proto.fl.coordinator_pb2 import (
+    EndTrainingRoundRequest,
+    EndTrainingRoundResponse,
+    HeartbeatRequest,
+    HeartbeatResponse,
+    RendezvousReply,
+    RendezvousRequest,
+    RendezvousResponse,
+    StartTrainingRoundRequest,
+    StartTrainingRoundResponse,
+    State,
+)
+from xain_proto.numproto import ndarray_to_proto, proto_to_ndarray
 
 from xain_fl.coordinator.participants import Participants
 from xain_fl.coordinator.round import Round
-from xain_fl.cproto import coordinator_pb2
 from xain_fl.fl.coordinator.aggregate import Aggregator, FederatedAveragingAgg
 from xain_fl.fl.coordinator.controller import Controller, RandomController
-from xain_fl.logger import get_logger
+from xain_fl.logger import StructLogger, get_logger
 from xain_fl.tools.exceptions import InvalidRequestError, UnknownParticipantError
 
-logger = get_logger(__name__)
+logger: StructLogger = get_logger(__name__)
 
 
-class Coordinator:
+# TODO: raise exceptions for invalid attribute values: https://xainag.atlassian.net/browse/XP-387
+class Coordinator:  # pylint: disable=too-many-instance-attributes
     """Class implementing the main Coordinator logic. It is implemented as a
     state machine that reacts to received messages.
 
     The states of the Coordinator are:
-        STANDBY: The coordinator is in standby mode, typically when waiting for
-        participants to connect. In this mode the only messages that the
-        coordinator can receive are :class:`~.coordinator_pb2.RendezvousRequest`
-        and :class:`~.coordinator_pb2.HeartbeatRequest`.
-        ROUND: A round is currently in progress. During a round the important
-        messages the coordinator can receive are
-        :class:`~.coordinator_pb2.StartTrainingRequest` and
-        :class:`~.coordinator_pb2.EndTrainingRequest`.
-        Since participants are selected for rounds or not, they can be advertised
-        either ROUND or STANDBY accordingly.
-        FINISHED: The training session has ended and participants should
-        disconnect from the coordinator.
-
-    States are exchanged during heartbeats so that both coordinators and
-    participants can react to each others state change.
+
+        - ``STANDBY``: The coordinator is in standby mode, typically
+          when waiting for participants to connect. In this mode the
+          only messages that the coordinator can receive are
+          :class:`~.coordinator_pb2.RendezvousRequest` and
+          :class:`~.coordinator_pb2.HeartbeatRequest`.
+
+        - ``ROUND``: A round is currently in progress. During a round
+          the important messages the coordinator can receive are
+          :class:`~.coordinator_pb2.StartTrainingRoundRequest` and
+          :class:`~.coordinator_pb2.EndTrainingRoundRequest`.  Since
+          participants are selected for rounds or not, they can be
+          advertised either ROUND or STANDBY accordingly.
+
+        - ``FINISHED``: The training session has ended and
+          participants should disconnect from the coordinator.
+
+    States are exchanged during heartbeats so that both coordinators
+    and participants can react to each others state change.
 
     The flow of the Coordinator:
+
         1. The coordinator is started and waits for enough participants to join. `STANDBY`.
         2. Once enough participants are connected the coordinator starts the rounds. `ROUND N`.
         3. Repeat step 2. for the given number of rounds
         4. The training session is over and the coordinator is ready to shutdown. `FINISHED`.
 
     Note:
+
         :class:`~.coordinator_pb2.RendezvousRequest` is always allowed
         regardless of which state the coordinator is on.
 
-    Constants:
-        DEFAULT_AGGREGATOR (:class:`~.Aggregator`): if no Aggregator instance is provided
-            during initialisation, then :class:`~.FederatedAveragingAgg` in used.
-        DEFAULT_CONTROLLER (:class:`~.Controller`): if no Controller instance is provided
-            during initialisation, then :class:`~.RandomController` in used.
-
-    Attributes:
-        num_rounds (:obj:`int`, optional): The number of rounds of the training
-            session. Defaults to 10.
-        minimum_participants_in_round (:obj:`int`, optional): The minimum number of
-            participants that participate in a round. Defaults to 1.
-        fraction_of_participants (:obj:`float`, optional): The fraction of total
-            connected participants to be selected in a single round. Defaults to 1.0,
-            meaning that all connected participants will be selected.
-        weights (:obj:`list` of :class:`~numpy.ndarray`, optional): The weights of
-            the global model. Defaults to [].
-        epochs (:obj:`int`, optional): Number of training iterations local to
-            Participant.  Defaults to 0.
-        epochs_base (:obj:`int`, optional): Global number of epochs as of last
-            round. Defaults to 0.
-        aggregator: (:class:`~.Aggregator`, optional): The type of aggregation
-            to perform at the end of each round. Defaults to :class:`~.FederatedAveragingAgg`.
-        controller: (:class:`~.Controller`, optional): Controls how the Participants
-            are selected at the start of each round. Defaults to :class:`~.RandomController`.
-        """
+    Args:
 
-    # pylint: disable-msg=too-many-instance-attributes
-    # pylint: disable-msg=dangerous-default-value
+        num_rounds: The number of rounds of the training session
 
-    DEFAULT_AGGREGATOR: Aggregator = FederatedAveragingAgg()
-    DEFAULT_CONTROLLER: Controller = RandomController(participant_ids=[])
+        minimum_participants_in_round: The minimum number of
+            participants that participate in a round
+
+        fraction_of_participants: The fraction of total connected
+            participants to be selected in a single round. Defaults to
+            1.0, meaning that all connected participants will be
+            selected. It must be in the (0.0, 1.0] interval.
+
+        weights: The weights of the global model.
+
+        epochs: Number of training iterations local to Participant.
+
+        epochs_base: Global number of epochs as of last round.
 
-    def __init__(
+        aggregator: The type of aggregation to perform at the end of
+            each round. Defaults to :class:`~.FederatedAveragingAgg`.
+
+        controller: Controls how the Participants are selected at the
+            start of each round. Defaults to
+            :class:`~.RandomController`.
+    """
+
+    DEFAULT_AGGREGATOR: Aggregator = FederatedAveragingAgg()
+    """
+    if no Aggregator instance is provided during initialisation, then
+    :class:`~.FederatedAveragingAgg` in used.
+    """
+
+    DEFAULT_CONTROLLER: Controller = RandomController()
+    """
+    if no Controller instance is provided during initialisation, then
+    :class:`~.RandomController` in used.
+    """
+
+    def __init__(  # pylint: disable=too-many-arguments,dangerous-default-value
         self,
         num_rounds: int = 1,
         minimum_participants_in_round: int = 1,
         fraction_of_participants: float = 1.0,
-        weights: List[ndarray] = [],
+        weights: List[ndarray] = [],  # TODO: change to non-dangerous default value
         epochs: int = 1,
         epoch_base: int = 0,
         aggregator: Optional[Aggregator] = None,
@@ -106,7 +132,7 @@ def __init__(
         self.round: Round = Round(self.participants.ids())
 
         # state variables
-        self.state: coordinator_pb2.State = coordinator_pb2.State.STANDBY
+        self.state: State = State.STANDBY
         self.current_round: int = 0
 
     def get_minimum_connected_participants(self) -> int:
@@ -121,17 +147,24 @@ def on_message(
         self, message: GeneratedProtocolMessageType, participant_id: str
     ) -> GeneratedProtocolMessageType:
         """Coordinator method that implements the state machine.
+
         Args:
-            message (:class:`~.GeneratedProtocolMessageType`): A protobuf message.
-            participant_id (:obj:`str`): The id of the participant making the request.
+
+            message: A protobuf message.
+            participant_id: The id of the participant making the request.
+
         Returns:
-            :class:`~.GeneratedProtocolMessageType`: The reply to be sent back to the participant.
+
+            The response sent back to the participant.
+
         Raises:
-            :class:`~UnknownParticipantError`: If it receives a request from an
-            unknown participant. Typically a participant that has not
-            rendezvous with the :class:`~.Coordinator`.
-            :class:`~InvalidRequestError`: If it receives a request that is not
-            allowed in the current :class:`~.Coordinator` state.
+
+            UnknownParticipantError: If it receives a request from an
+                unknown participant. Typically a participant that has not
+                rendezvous with the :class:`~.Coordinator`.
+
+            InvalidRequestError: If it receives a request that is not
+                allowed in the current :class:`~.Coordinator` state.
         """
         logger.debug(
             "Received message from participant",
@@ -142,7 +175,7 @@ def on_message(
         # Unless this is a RendezvousRequest the coordinator should not accept messages
         # from participants that have not been accepted
         if (
-            not isinstance(message, coordinator_pb2.RendezvousRequest)
+            not isinstance(message, RendezvousRequest)
             and participant_id not in self.participants.ids()
         ):
             raise UnknownParticipantError(
@@ -150,67 +183,68 @@ def on_message(
                 "Please try to rendezvous with the coordinator before making a request."
             )
 
-        # pylint: disable-msg=no-else-return
-        if isinstance(message, coordinator_pb2.RendezvousRequest):
+        if isinstance(message, RendezvousRequest):
             # Handle rendezvous
             return self._handle_rendezvous(message, participant_id)
 
-        elif isinstance(message, coordinator_pb2.HeartbeatRequest):
+        if isinstance(message, HeartbeatRequest):
             # Handle heartbeat
             return self._handle_heartbeat(message, participant_id)
 
-        elif isinstance(message, coordinator_pb2.StartTrainingRequest):
+        if isinstance(message, StartTrainingRoundRequest):
             # handle start training
-            return self._handle_start_training(message, participant_id)
+            return self._handle_start_training_round(message, participant_id)
 
-        elif isinstance(message, coordinator_pb2.EndTrainingRequest):
+        if isinstance(message, EndTrainingRoundRequest):
             # handle end training
-            return self._handle_end_training(message, participant_id)
+            return self._handle_end_training_round(message, participant_id)
 
-        else:
-            raise NotImplementedError
+        raise NotImplementedError
 
     def remove_participant(self, participant_id: str) -> None:
         """Remove a participant from the list of accepted participants.
-        This method is to be called when it is detected that a participant has
-        disconnected.
-        After a participant is removed, if the number of remaining participants
-        is less than the minimum number of participants that need to be connected,
-        the :class:`~.Coordinator` will transition to STANDBY state.
+
+        This method is to be called when it is detected that a
+        participant has disconnected. After a participant is removed,
+        if the number of remaining participants is less than the
+        minimum number of participants that need to be connected, the
+        :class:`~.Coordinator` will transition to STANDBY state.
+
         Args:
-            participant_id (:obj:`str`): The id of the participant to remove.
+
+            participant_id: The id of the participant to remove.
+
         """
         self.participants.remove(participant_id)
         logger.info("Removing participant", participant_id=participant_id)
 
         if self.participants.len() < self.minimum_connected_participants:
-            self.state = coordinator_pb2.State.STANDBY
+            self.state = State.STANDBY
 
     def select_participant_ids_and_init_round(self) -> None:
-        """Initiates the Controller, selects ids and initiates a Round.
+        """Selects the participant ids and initiates a Round.
         """
-        self.controller = RandomController(
-            participant_ids=self.participants.ids(),
-            fraction_of_participants=self.fraction_of_participants,
-        )
-        selected_ids = self.controller.select_ids()
+        self.controller.fraction_of_participants = self.fraction_of_participants
+        selected_ids = self.controller.select_ids(self.participants.ids())
         self.round = Round(selected_ids)
 
     def _handle_rendezvous(
-        self, _message: coordinator_pb2.RendezvousRequest, participant_id: str
-    ) -> coordinator_pb2.RendezvousReply:
+        self, _message: RendezvousRequest, participant_id: str
+    ) -> RendezvousResponse:
         """Handles a Rendezvous request.
+
         Args:
-            _message (:class:`~.coordinator_pb2.RendezvousRequest`): The
-                request to handle. Currently not used.
-            participant_id (:obj:`str`): The id of the participant making the
-                request.
+
+            _message: The request to handle. Currently not used.
+            participant_id: The id of the participant making the request.
+
         Returns:
-            :class:`~.coordinator_pb2.RendezvousReply`: The reply to the participant.
+
+            The response to the participant.
         """
 
         if self.participants.len() < self.minimum_connected_participants:
-            response = coordinator_pb2.RendezvousResponse.ACCEPT
+            reply = RendezvousReply.ACCEPT
             self.participants.add(participant_id)
             logger.info(
                 "Accepted participant",
@@ -224,117 +258,121 @@ def _handle_rendezvous(
                 self.select_participant_ids_and_init_round()
 
                 # TODO: We may need to make this update thread safe
-                self.state = coordinator_pb2.State.ROUND
-                self.current_round = (
-                    1 if self.current_round == 0 else self.current_round
-                )
+                self.state = State.ROUND
+                self.current_round = 1 if self.current_round == 0 else self.current_round
         else:
-            response = coordinator_pb2.RendezvousResponse.LATER
+            reply = RendezvousReply.LATER
             logger.info(
                 "Reject participant",
                 participant_id=participant_id,
                 current_participants_count=self.participants.len(),
             )
 
-        return coordinator_pb2.RendezvousReply(response=response)
+        return RendezvousResponse(reply=reply)
 
     def _handle_heartbeat(
-        self, _message: coordinator_pb2.HeartbeatRequest, participant_id: str
-    ) -> coordinator_pb2.HeartbeatReply:
+        self, _message: HeartbeatRequest, participant_id: str
+    ) -> HeartbeatResponse:
         """Handles a Heartbeat request.
-        It checks if a participant has been selected, if it has,
-        returns ROUND state to them, else STANDBY.
+
+        Responds to the participant with:
+
+            - ``FINISHED``: if coordinator is in state FINISHED,
+            - ``ROUND``: if the participant is selected for the current round,
+            - ``STANDBY``: if the participant is not selected for the current round.
+
         Args:
-            _message (:class:`~.coordinator_pb2.HeartbeatRequest`): The
-                request to handle. Currently not used.
-            participant_id (:obj:`str`): The id of the participant making the
-                request.
+
+            _message: The request to handle. Currently not used.
+            participant_id: The id of the participant making the request.
+
         Returns:
-            :class:`~.coordinator_pb2.HeartbeatReply`: The reply to the participant.
+
+            The response to the participant.
         """
         self.participants.update_expires(participant_id)
 
-        if participant_id in self.round.participant_ids:
-            state = coordinator_pb2.State.ROUND
+        if self.state == State.FINISHED or participant_id in self.round.participant_ids:
+            state = self.state
         else:
-            state = coordinator_pb2.State.STANDBY
+            state = State.STANDBY
 
-        # send heartbeat reply advertising the current state
-        return coordinator_pb2.HeartbeatReply(state=state, round=self.current_round)
+        # send heartbeat response advertising the current state
+        logger.debug(
+            "Heartbeat response",
+            participant_id=participant_id,
+            message=state,
+            round=self.current_round,
+        )
+        return HeartbeatResponse(state=state, round=self.current_round)
+
+    def _handle_start_training_round(
+        self, _message: StartTrainingRoundRequest, participant_id: str
+    ) -> StartTrainingRoundResponse:
+        """Handles a StartTrainingRound request.
 
-    def _handle_start_training(
-        self, _message: coordinator_pb2.StartTrainingRequest, participant_id: str
-    ) -> coordinator_pb2.StartTrainingReply:
-        """Handles a StartTraining request.
         Args:
-            _message (:class:`~.coordinator_pb2.StartTrainingRequest`): The
-                request to handle. Currently not used.
-            participant_id (:obj:`str`): The id of the participant making the
-                request.
+            _message: The request to handle. Currently not used.
+            participant_id: The id of the participant making the request.
         Returns:
-            :class:`~.coordinator_pb2.StartTrainingReply`: The reply to the participant.
+            :class:`~.coordinator_pb2.StartTrainingRoundResponse`: The response to the participant.
         """
-        # The coordinator should only accept StartTraining requests if it is
+        # The coordinator should only accept StartTrainingRound requests if it is
         # in the ROUND state and when the participant has been selected for the round.
-        coordinator_not_in_a_round = self.state != coordinator_pb2.State.ROUND
+        coordinator_not_in_a_round = self.state != State.ROUND
         participant_not_selected = participant_id not in self.round.participant_ids
         if coordinator_not_in_a_round or participant_not_selected:
             raise InvalidRequestError(
                 f"Participant {participant_id} sent a "
-                "StartTrainingRequest outside of a round"
+                "StartTrainingRoundRequest outside of a round"
             )
 
         weights_proto = [ndarray_to_proto(nda) for nda in self.weights]
 
-        return coordinator_pb2.StartTrainingReply(
-            weights=weights_proto, epochs=self.epochs, epoch_base=self.epoch_base
+        return StartTrainingRoundResponse(
+            weights=weights_proto, epochs=self.epochs, epoch_base=self.epoch_base,
         )
 
-    def _handle_end_training(
-        self, message: coordinator_pb2.EndTrainingRequest, participant_id: str
-    ) -> coordinator_pb2.EndTrainingReply:
-        """Handles a EndTraining request.
+    def _handle_end_training_round(
+        self, message: EndTrainingRoundRequest, participant_id: str
+    ) -> EndTrainingRoundResponse:
+        """Handles a EndTrainingRound request.
+
         Args:
-            message (:class:`~.coordinator_pb2.EndTrainingRequest`): The request to handle.
-            participant_id (:obj:`str`): The id of the participant making the request.
+
+            message: The request to handle.
+            participant_id: The id of the participant making the request.
+
         Returns:
-            :class:`~.coordinator_pb2.EndTrainingReply`: The reply to the participant.
+
+            The response to the participant.
         """
 
         # TODO: Ideally we want to know for which round the participant is
         # submitting the updates and raise an exception if it is the wrong
         # round.
-        weights_proto, number_samples, metrics_proto = (
-            message.weights,
-            message.number_samples,
-            message.metrics,
-        )
 
         # record the request data
         weight_update: Tuple[List[ndarray], int] = (
-            [proto_to_ndarray(pnda) for pnda in weights_proto],
-            number_samples,
+            [proto_to_ndarray(pnda) for pnda in message.weights],
+            message.number_samples,
         )
-        metrics: Dict[str, List[ndarray]] = {
-            k: [proto_to_ndarray(v) for v in mv.metrics]
-            for k, mv in metrics_proto.items()
-        }
+        metrics: Dict[str, ndarray] = {k: proto_to_ndarray(v) for k, v in message.metrics.items()}
         self.round.add_updates(participant_id, weight_update, metrics)
 
         # The round is over. Run the aggregation
         if self.round.is_finished():
-            logger.info(
-                "Running aggregation for round", current_round=self.current_round
-            )
+            logger.info("Running aggregation for round", current_round=self.current_round)
 
             self.weights = self.aggregator.aggregate(self.round.get_weight_updates())
 
             # update the round or finish the training session
             if self.current_round == self.num_rounds:
-                self.state = coordinator_pb2.State.FINISHED
+                logger.debug("Last round over", round=self.current_round)
+                self.state = State.FINISHED
             else:
                 self.current_round += 1
                 # reinitialize the round
                 self.select_participant_ids_and_init_round()
 
-        return coordinator_pb2.EndTrainingReply()
+        return EndTrainingRoundResponse()
diff --git a/xain_fl/coordinator/coordinator_grpc.py b/xain_fl/coordinator/coordinator_grpc.py
index b79e7cde8..07a3e5eaf 100644
--- a/xain_fl/coordinator/coordinator_grpc.py
+++ b/xain_fl/coordinator/coordinator_grpc.py
@@ -1,7 +1,21 @@
+"""XAIN FL gRPC Coordinator"""
+
 import grpc
+from xain_proto.fl.coordinator_pb2 import (
+    EndTrainingRoundRequest,
+    EndTrainingRoundResponse,
+    HeartbeatRequest,
+    HeartbeatResponse,
+    RendezvousRequest,
+    RendezvousResponse,
+    StartTrainingRoundRequest,
+    StartTrainingRoundResponse,
+    State,
+)
+from xain_proto.fl.coordinator_pb2_grpc import CoordinatorServicer
 
 from xain_fl.coordinator.coordinator import Coordinator
-from xain_fl.cproto import coordinator_pb2, coordinator_pb2_grpc
+from xain_fl.coordinator.store import Store
 from xain_fl.tools.exceptions import (
     DuplicatedUpdateError,
     InvalidRequestError,
@@ -9,7 +23,7 @@
 )
 
 
-class CoordinatorGrpc(coordinator_pb2_grpc.CoordinatorServicer):
+class CoordinatorGrpc(CoordinatorServicer):
     """The Coordinator gRPC service.
 
     The main logic for the Coordinator is decoupled from gRPC and implemented in the
@@ -18,16 +32,21 @@ class CoordinatorGrpc(coordinator_pb2_grpc.CoordinatorServicer):
     :class:`xain_fl.coordinator.coordinator.Coordinator`.
 
     Args:
-        coordinator (:class:`xain_fl.coordinator.coordinator.Coordinator`): The Coordinator
-         state machine.
+
+        coordinator: The Coordinator state machine.
+
+        store: The Store in which the coordinator fetches trained
+            models from the participants and to which it saves
+            aggregated models.
     """
 
-    def __init__(self, coordinator: Coordinator):
+    def __init__(self, coordinator: Coordinator, store: Store):
         self.coordinator: Coordinator = coordinator
+        self.store: Store = store
 
     def Rendezvous(
-        self, request: coordinator_pb2.RendezvousRequest, context: grpc.ServicerContext
-    ) -> coordinator_pb2.RendezvousReply:
+        self, request: RendezvousRequest, context: grpc.ServicerContext
+    ) -> RendezvousResponse:
         """The Rendezvous gRPC method.
 
         A participant contacts the coordinator and the coordinator adds the
@@ -35,111 +54,124 @@ def Rendezvous(
         all the participants it tells the participant to try again later.
 
         Args:
-            request (:class:`~.coordinator_pb2.RendezvousRequest`): The participant's request.
-            context (:class:`~grpc.ServicerContext`): The context associated with the gRPC request.
+
+            request: The participant's request.
+            context: The context associated with the gRPC request.
 
         Returns:
-            :class:`~.coordinator_pb2.RendezvousReply`: The reply to the
-            participant's request. The reply is an enum containing either:
 
-                ACCEPT: If the :class:`xain_fl.coordinator.coordinator.Coordinator`
-                    does not have enough participants.
-                LATER: If the :class:`xain_fl.coordinator.coordinator.Coordinator`
+            The response to the participant's request. The response is
+            an enum containing either:
+
+                - `ACCEPT`: If the
+                  :class:`xain_fl.coordinator.coordinator.Coordinator`
+                  does not have enough participants.
+
+                - `LATER`: If the
+                    :class:`xain_fl.coordinator.coordinator.Coordinator`
                     already has enough participants.
         """
         return self.coordinator.on_message(request, context.peer())
 
     def Heartbeat(
-        self, request: coordinator_pb2.HeartbeatRequest, context: grpc.ServicerContext
-    ) -> coordinator_pb2.HeartbeatReply:
+        self, request: HeartbeatRequest, context: grpc.ServicerContext
+    ) -> HeartbeatResponse:
         """The Heartbeat gRPC method.
 
         Participants periodically send an heartbeat so that the
         :class:`Coordinator` can detect failures.
 
         Args:
-            request (:class:`~.coordinator_pb2.HeartbeatRequest`): The
-                participant's request. The participant's request contains the
-                current :class:`~.coordinator_pb2.State` and round number the
+
+            request: The participant's request. The participant's
+                request contains the current
+                :class:`~.coordinator_pb2.State` and round number the
                 participant is on.
-            context (:class:`~grpc.ServicerContext`): The context associated
-                with the gRPC request.
+
+            context: The context associated with the gRPC request.
 
         Returns:
-            :class:`~.coordinator_pb2.HeartbeatReply`: The reply to the
-            participant's request. The reply contains both the
-            :class:`~.coordinator_pb2.State` and the current round the
-            coordinator is on. If a training session has not started yet the
-            round number defaults to 0.
+
+            The response to the participant's request. The response
+            contains both the :class:`~.coordinator_pb2.State` and the
+            current round the coordinator is on. If a training session
+            has not started yet the round number defaults to 0.
         """
         try:
             return self.coordinator.on_message(request, context.peer())
         except UnknownParticipantError as error:
             context.set_details(str(error))
             context.set_code(grpc.StatusCode.PERMISSION_DENIED)
-            return coordinator_pb2.HeartbeatReply()
+            return HeartbeatResponse()
 
-    def StartTraining(
-        self,
-        request: coordinator_pb2.StartTrainingRequest,
-        context: grpc.ServicerContext,
-    ) -> coordinator_pb2.StartTrainingReply:
-        """The StartTraining gRPC method.
+    def StartTrainingRound(
+        self, request: StartTrainingRoundRequest, context: grpc.ServicerContext,
+    ) -> StartTrainingRoundResponse:
+        """The StartTrainingRound gRPC method.
 
         Once a participant is notified that the :class:`xain_fl.coordinator.coordinator.Coordinator`
         is in a round (through the state advertised in the
-        :class:`~.coordinator_pb2.HeartbeatReply`), the participant should call this
+        :class:`~.coordinator_pb2.HeartbeatResponse`), the participant should call this
         method in order to get the global model weights in order to start the
         training for the round.
 
         Args:
-            request (:class:`~.coordinator_pb2.StartTrainingRequest`): The participant's request.
-            context (:class:`~grpc.ServicerContext`): The context associated with the gRPC request.
+
+            request: The participant's request.
+            context: The context associated with the gRPC request.
 
         Returns:
-            :class:`~.coordinator_pb2.StartTrainingReply`: The reply to the
-            participant's request. The reply contains the global model weights.
-            """
+
+            The response to the participant's request. The response
+            contains the global model weights.
+        """
         try:
             return self.coordinator.on_message(request, context.peer())
         except UnknownParticipantError as error:
             context.set_details(str(error))
             context.set_code(grpc.StatusCode.PERMISSION_DENIED)
-            return coordinator_pb2.StartTrainingReply()
+            return StartTrainingRoundResponse()
         except InvalidRequestError as error:
             context.set_details(str(error))
             context.set_Code(grpc.StatusCode.FAILED_PRECONDITION)
-            return coordinator_pb2.StartTrainingReply()
+            return StartTrainingRoundResponse()
 
-    def EndTraining(
-        self, request: coordinator_pb2.EndTrainingRequest, context: grpc.ServicerContext
-    ) -> coordinator_pb2.EndTrainingReply:
-        """The EndTraining gRPC method.
+    def EndTrainingRound(
+        self, request: EndTrainingRoundRequest, context: grpc.ServicerContext
+    ) -> EndTrainingRoundResponse:
+        """The EndTrainingRound gRPC method.
 
         Once a participant has finished the training for the round it calls this
         method in order to submit to the :class:`xain_fl.coordinator.coordinator.Coordinator`
         the updated weights.
 
         Args:
-            request (:class:`~.coordinator_pb2.EndTrainingRequest`): The
-                participant's request. The request contains the updated weights as
-                a result of the training as well as any metrics helpful for the
+
+            request: The participant's request. The request contains
+                the updated weights as a result of the training as
+                well as any metrics helpful for the
                 :class:`xain_fl.coordinator.coordinator.Coordinator`.
-            context (:class:`~grpc.ServicerContext`): The context associated with the gRPC request.
+
+            context: The context associated with the gRPC request.
 
         Returns:
-            :class:`~.coordinator_pb2.EndTrainingReply`: The reply to the
-            participant's request. The reply is just an acknowledgment that
-            the :class:`xain_fl.coordinator.coordinator.Coordinator` successfully received
-            the updated weights.
+
+            The response to the participant's request. The response is
+            just an acknowledgment that the
+            :class:`xain_fl.coordinator.coordinator.Coordinator`
+            successfully received the updated weights.
         """
         try:
-            return self.coordinator.on_message(request, context.peer())
+            response = self.coordinator.on_message(request, context.peer())
         except DuplicatedUpdateError as error:
             context.set_details(str(error))
             context.set_code(grpc.StatusCode.ALREADY_EXISTS)
-            return coordinator_pb2.EndTrainingReply()
+            return EndTrainingRoundResponse()
         except UnknownParticipantError as error:
             context.set_details(str(error))
             context.set_code(grpc.StatusCode.PERMISSION_DENIED)
-            return coordinator_pb2.EndTrainingReply()
+            return EndTrainingRoundResponse()
+
+        if self.coordinator.state == State.FINISHED:
+            self.store.write_weights(self.coordinator.current_round, self.coordinator.weights)
+        return response
diff --git a/xain_fl/coordinator/heartbeat.py b/xain_fl/coordinator/heartbeat.py
index 93d39b760..b50ff6d4b 100644
--- a/xain_fl/coordinator/heartbeat.py
+++ b/xain_fl/coordinator/heartbeat.py
@@ -1,29 +1,31 @@
+"""XAIN FL Hearbeats"""
+
 import threading
 import time
 from typing import List
 
 from xain_fl.coordinator.coordinator import Coordinator
-from xain_fl.logger import get_logger
+from xain_fl.logger import StructLogger, get_logger
 
-logger = get_logger(__name__)
+logger: StructLogger = get_logger(__name__)
 
 
-def monitor_heartbeats(
-    coordinator: Coordinator, terminate_event: threading.Event
-) -> None:
+def monitor_heartbeats(coordinator: Coordinator, terminate_event: threading.Event) -> None:
     """Monitors the heartbeat of participants.
 
     If a heartbeat expires the participant is removed from the :class:`~.Participants`.
 
     Note:
+
         This is meant to be run inside a thread and expects an
         :class:`~threading.Event`, to know when it should terminate.
 
     Args:
-        coordinator (:class:`xain_fl.coordinator.coordinator.Coordinator`): The coordinator
-            to monitor for heartbeats.
-        terminate_event (:class:`~threading.Event`): A threading event to signal
-            that this method should terminate.
+
+        coordinator: The coordinator to monitor for heartbeats.
+
+        terminate_event: A threading event to signal that this method
+            should terminate.
     """
 
     logger.info("Heartbeat monitor starting...")
diff --git a/xain_fl/coordinator/participants.py b/xain_fl/coordinator/participants.py
index 71652e609..ac4fe5cd2 100644
--- a/xain_fl/coordinator/participants.py
+++ b/xain_fl/coordinator/participants.py
@@ -1,3 +1,5 @@
+"""XAIN FL Participants"""
+
 import threading
 import time
 from typing import Dict, List
@@ -5,15 +7,16 @@
 from xain_fl.coordinator import HEARTBEAT_TIME, HEARTBEAT_TIMEOUT
 
 
-class ParticipantContext:
+class ParticipantContext:  # pylint: disable=too-few-public-methods
     """Class to store state about each participant. Currently it only stores the `participant_id`
     and the time when the next heartbeat_expires.
 
     In the future we may store more information like in what state a participant is in e.g.
-    IDLE, RUNNING, ...
+    `IDLE`, `RUNNING`, ...
+
+    Args:
 
-    Attributes:
-        participant_id (:obj:`str`): The id of the participant. Typically a
+        participant_id: The id of the participant. Typically a
             host:port or public key when using SSL.
     """
 
@@ -36,7 +39,8 @@ def add(self, participant_id: str) -> None:
         """Adds a new participant to the list of participants.
 
         Args:
-            participant_id (:obj:`str`): The id of the participant to add.
+
+            participant_id: The id of the participant to add.
         """
 
         with self._lock:
@@ -45,10 +49,12 @@ def add(self, participant_id: str) -> None:
     def remove(self, participant_id: str) -> None:
         """Removes a participant from the list of participants.
 
-        This will be typically used after a participant is disconnected from the coordinator.
+        This will be typically used after a participant is
+        disconnected from the coordinator.
 
         Args:
-            participant_id (:obj:`str`): The id of the participant to remove.
+
+            participant_id: The id of the participant to remove.
         """
 
         with self._lock:
@@ -62,7 +68,8 @@ def next_expiration(self) -> float:
         the next check.
 
         Returns:
-            :obj:`float`: The next heartbeat to expire.
+
+            The next heartbeat to expire.
         """
 
         with self._lock:
@@ -75,7 +82,8 @@ def len(self) -> int:
         """Get the number of participants.
 
         Returns:
-            :obj:`int`: The number of participants in the list.
+
+            The number of participants in the list.
         """
 
         with self._lock:
@@ -85,7 +93,8 @@ def ids(self) -> List[str]:
         """Get the ids of the participants.
 
         Returns:
-            :obj:`list` of :obj:`str`: The list of participant ids.
+
+            The list of participant ids.
         """
 
         with self._lock:
@@ -98,7 +107,8 @@ def update_expires(self, participant_id: str) -> None:
         every time a participant sends a heartbeat.
 
         Args:
-            participant_id (:obj:`str`): The id of the participant to update the expire time.
+
+            participant_id: The id of the participant to update the expire time.
         """
 
         with self._lock:
diff --git a/xain_fl/coordinator/round.py b/xain_fl/coordinator/round.py
index 3cb7b8731..cf80bc16b 100644
--- a/xain_fl/coordinator/round.py
+++ b/xain_fl/coordinator/round.py
@@ -1,3 +1,5 @@
+"""XAIN FL Rounds"""
+
 from typing import Dict, List, Tuple
 
 from numpy import ndarray
@@ -11,9 +13,10 @@ class Round:
     participants during a round and does some sanity checks like preventing the
     same participant to submit multiple updates during a single round.
 
-    Attributes:
-        participant_ids(:obj:`list` of :obj:`str`): The list of IDs of the participants
-            selected to participate in this round.
+    Args:
+
+        participant_ids: The list of IDs of the participants selected
+            to participate in this round.
     """
 
     def __init__(self, participant_ids: List[str]) -> None:
@@ -24,18 +27,21 @@ def add_updates(
         self,
         participant_id: str,
         weight_update: Tuple[List[ndarray], int],
-        metrics: Dict[str, List[ndarray]],
+        metrics: Dict[str, ndarray],
     ) -> None:
         """Valid a participant's update for the round.
 
         Args:
-            participant_id (:obj:`str`): The id of the participant making the request.
-            weight_update (:obj:`tuple` of :obj:`list` of :class:`~numpy.ndarray`):
-                A tuple containing a list of updated weights.
-            metrics (:obj:`dict`): A dictionary containing metrics with the name and the value
-                as list of ndarrays.
+
+            participant_id: The id of the participant making the request.
+
+            weight_update: A tuple containing a list of updated weights.
+
+            metrics: A dictionary containing metrics with the name and
+                the value as list of ndarrays.
 
         Raises:
+
             DuplicatedUpdateError: If the participant already submitted his update this round.
         """
 
@@ -54,8 +60,9 @@ def is_finished(self) -> bool:
         If all participants submitted their updates the round is considered finished.
 
         Returns:
-            :obj:`bool`:: :obj:`True` if all participants submitted their
-            updates this round. :obj:`False` otherwise.
+
+            `True` if all participants submitted their updates this
+            round. `False` otherwise.
         """
         return len(self.updates) == len(self.participant_ids)
 
@@ -64,7 +71,7 @@ def get_weight_updates(self) -> List[Tuple[List[ndarray], int]]:
         This list will usually be used by the aggregation function.
 
         Returns:
-            :obj:`list` of :obj:`tuple`: The list of weight updates from all
-            participants.
+
+            The list of weight updates from all participants.
         """
         return [v["weight_update"] for k, v in self.updates.items()]
diff --git a/xain_fl/coordinator/store.py b/xain_fl/coordinator/store.py
new file mode 100644
index 000000000..564dae44e
--- /dev/null
+++ b/xain_fl/coordinator/store.py
@@ -0,0 +1,43 @@
+# pylint: disable=missing-docstring
+from io import BytesIO
+import pickle
+
+import boto3
+from numpy import ndarray
+
+
+# pylint: disable=too-few-public-methods
+class StoreConfig:
+    def __init__(
+        self, endpoint_url: str, access_key_id: str, secret_access_key: str, bucket: str,
+    ):
+        self.endpoint_url = endpoint_url
+        self.access_key_id = access_key_id
+        self.secret_access_key = secret_access_key
+        self.bucket = bucket
+
+
+class Store:
+    def __init__(self, config: StoreConfig):
+        self.config = config
+        # pylint: disable=invalid-name
+        self.s3 = boto3.resource(
+            "s3",
+            endpoint_url=self.config.endpoint_url,
+            aws_access_key_id=self.config.access_key_id,
+            aws_secret_access_key=self.config.secret_access_key,
+            # FIXME: not sure what this should be for now
+            region_name="dummy",
+        )
+
+    def write_weights(self, round: int, weights: ndarray):
+        bucket = self.s3.Bucket(self.config.bucket)
+        bucket.put_object(Body=pickle.dumps(weights), Key=str(round))
+
+    def read_weights(self, round: int) -> ndarray:
+        bucket = self.s3.Bucket(self.config.bucket)
+        data = BytesIO()
+        bucket.download_fileobj(str(round), data)
+        # FIXME: not sure whether getvalue() copies the data. If so we
+        # should probably prefer getbuffer() instead.
+        return pickle.loads(data.getvalue())
diff --git a/xain_fl/cproto/__init__.py b/xain_fl/cproto/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/xain_fl/cproto/conftest.py b/xain_fl/cproto/conftest.py
deleted file mode 100644
index bb7dfd2f3..000000000
--- a/xain_fl/cproto/conftest.py
+++ /dev/null
@@ -1,43 +0,0 @@
-from concurrent import futures
-
-import grpc
-import pytest
-
-from xain_fl.coordinator.coordinator import Coordinator
-from xain_fl.coordinator.coordinator_grpc import CoordinatorGrpc
-from xain_fl.cproto import coordinator_pb2_grpc, hellonumproto_pb2_grpc
-from xain_fl.cproto.numproto_server import NumProtoServer
-
-
-@pytest.fixture
-def greeter_server():
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
-    hellonumproto_pb2_grpc.add_NumProtoServerServicer_to_server(
-        NumProtoServer(), server
-    )
-    server.add_insecure_port("localhost:50051")
-    server.start()
-    yield
-    server.stop(0)
-
-
-@pytest.fixture
-def coordinator_service():
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
-    coordinator = Coordinator(
-        minimum_participants_in_round=10, fraction_of_participants=1.0
-    )
-    coordinator_grpc = CoordinatorGrpc(coordinator)
-    coordinator_pb2_grpc.add_CoordinatorServicer_to_server(coordinator_grpc, server)
-    server.add_insecure_port("localhost:50051")
-    server.start()
-    yield coordinator_grpc
-    server.stop(0)
-
-
-@pytest.fixture
-def participant_stub():
-    channel = grpc.insecure_channel("localhost:50051")
-    stub = coordinator_pb2_grpc.CoordinatorStub(channel)
-
-    return stub
diff --git a/xain_fl/cproto/coordinator_pb2.py b/xain_fl/cproto/coordinator_pb2.py
deleted file mode 100644
index 88f88c3df..000000000
--- a/xain_fl/cproto/coordinator_pb2.py
+++ /dev/null
@@ -1,579 +0,0 @@
-# -*- coding: utf-8 -*-
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: xain_fl/cproto/coordinator.proto
-
-import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
-from google.protobuf.internal import enum_type_wrapper
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
-from google.protobuf import symbol_database as _symbol_database
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-from numproto.protobuf import ndarray_pb2 as numproto_dot_protobuf_dot_ndarray__pb2
-
-
-DESCRIPTOR = _descriptor.FileDescriptor(
-  name='xain_fl/cproto/coordinator.proto',
-  package='xain.protobuf.coordinator',
-  syntax='proto3',
-  serialized_options=None,
-  serialized_pb=_b('\n xain_fl/cproto/coordinator.proto\x12\x19xain.protobuf.coordinator\x1a\x1fnumproto/protobuf/ndarray.proto\"\x13\n\x11RendezvousRequest\"R\n\x0fRendezvousReply\x12?\n\x08response\x18\x01 \x01(\x0e\x32-.xain.protobuf.coordinator.RendezvousResponse\"R\n\x10HeartbeatRequest\x12/\n\x05state\x18\x01 \x01(\x0e\x32 .xain.protobuf.coordinator.State\x12\r\n\x05round\x18\x02 \x01(\x05\"P\n\x0eHeartbeatReply\x12/\n\x05state\x18\x01 \x01(\x0e\x32 .xain.protobuf.coordinator.State\x12\r\n\x05round\x18\x02 \x01(\x05\"\x16\n\x14StartTrainingRequest\"e\n\x12StartTrainingReply\x12+\n\x07weights\x18\x01 \x03(\x0b\x32\x1a.numproto.protobuf.NDArray\x12\x0e\n\x06\x65pochs\x18\x02 \x01(\x05\x12\x12\n\nepoch_base\x18\x03 \x01(\x05\"\xc5\x02\n\x12\x45ndTrainingRequest\x12+\n\x07weights\x18\x01 \x03(\x0b\x32\x1a.numproto.protobuf.NDArray\x12\x16\n\x0enumber_samples\x18\x02 \x01(\x05\x12K\n\x07metrics\x18\x03 \x03(\x0b\x32:.xain.protobuf.coordinator.EndTrainingRequest.MetricsEntry\x1a\x65\n\x0cMetricsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12\x44\n\x05value\x18\x02 \x01(\x0b\x32\x35.xain.protobuf.coordinator.EndTrainingRequest.Metrics:\x02\x38\x01\x1a\x36\n\x07Metrics\x12+\n\x07metrics\x18\x01 \x03(\x0b\x32\x1a.numproto.protobuf.NDArray\"\x12\n\x10\x45ndTrainingReply*+\n\x12RendezvousResponse\x12\n\n\x06\x41\x43\x43\x45PT\x10\x00\x12\t\n\x05LATER\x10\x01*F\n\x05State\x12\x0b\n\x07STANDBY\x10\x00\x12\t\n\x05ROUND\x10\x01\x12\x0c\n\x08\x46INISHED\x10\x02\x12\t\n\x05READY\x10\x03\x12\x0c\n\x08TRAINING\x10\x04\x32\xbe\x03\n\x0b\x43oordinator\x12h\n\nRendezvous\x12,.xain.protobuf.coordinator.RendezvousRequest\x1a*.xain.protobuf.coordinator.RendezvousReply\"\x00\x12\x65\n\tHeartbeat\x12+.xain.protobuf.coordinator.HeartbeatRequest\x1a).xain.protobuf.coordinator.HeartbeatReply\"\x00\x12q\n\rStartTraining\x12/.xain.protobuf.coordinator.StartTrainingRequest\x1a-.xain.protobuf.coordinator.StartTrainingReply\"\x00\x12k\n\x0b\x45ndTraining\x12-.xain.protobuf.coordinator.EndTrainingRequest\x1a+.xain.protobuf.coordinator.EndTrainingReply\"\x00\x62\x06proto3')
-  ,
-  dependencies=[numproto_dot_protobuf_dot_ndarray__pb2.DESCRIPTOR,])
-
-_RENDEZVOUSRESPONSE = _descriptor.EnumDescriptor(
-  name='RendezvousResponse',
-  full_name='xain.protobuf.coordinator.RendezvousResponse',
-  filename=None,
-  file=DESCRIPTOR,
-  values=[
-    _descriptor.EnumValueDescriptor(
-      name='ACCEPT', index=0, number=0,
-      serialized_options=None,
-      type=None),
-    _descriptor.EnumValueDescriptor(
-      name='LATER', index=1, number=1,
-      serialized_options=None,
-      type=None),
-  ],
-  containing_type=None,
-  serialized_options=None,
-  serialized_start=842,
-  serialized_end=885,
-)
-_sym_db.RegisterEnumDescriptor(_RENDEZVOUSRESPONSE)
-
-RendezvousResponse = enum_type_wrapper.EnumTypeWrapper(_RENDEZVOUSRESPONSE)
-_STATE = _descriptor.EnumDescriptor(
-  name='State',
-  full_name='xain.protobuf.coordinator.State',
-  filename=None,
-  file=DESCRIPTOR,
-  values=[
-    _descriptor.EnumValueDescriptor(
-      name='STANDBY', index=0, number=0,
-      serialized_options=None,
-      type=None),
-    _descriptor.EnumValueDescriptor(
-      name='ROUND', index=1, number=1,
-      serialized_options=None,
-      type=None),
-    _descriptor.EnumValueDescriptor(
-      name='FINISHED', index=2, number=2,
-      serialized_options=None,
-      type=None),
-    _descriptor.EnumValueDescriptor(
-      name='READY', index=3, number=3,
-      serialized_options=None,
-      type=None),
-    _descriptor.EnumValueDescriptor(
-      name='TRAINING', index=4, number=4,
-      serialized_options=None,
-      type=None),
-  ],
-  containing_type=None,
-  serialized_options=None,
-  serialized_start=887,
-  serialized_end=957,
-)
-_sym_db.RegisterEnumDescriptor(_STATE)
-
-State = enum_type_wrapper.EnumTypeWrapper(_STATE)
-ACCEPT = 0
-LATER = 1
-STANDBY = 0
-ROUND = 1
-FINISHED = 2
-READY = 3
-TRAINING = 4
-
-
-
-_RENDEZVOUSREQUEST = _descriptor.Descriptor(
-  name='RendezvousRequest',
-  full_name='xain.protobuf.coordinator.RendezvousRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=96,
-  serialized_end=115,
-)
-
-
-_RENDEZVOUSREPLY = _descriptor.Descriptor(
-  name='RendezvousReply',
-  full_name='xain.protobuf.coordinator.RendezvousReply',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='response', full_name='xain.protobuf.coordinator.RendezvousReply.response', index=0,
-      number=1, type=14, cpp_type=8, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=117,
-  serialized_end=199,
-)
-
-
-_HEARTBEATREQUEST = _descriptor.Descriptor(
-  name='HeartbeatRequest',
-  full_name='xain.protobuf.coordinator.HeartbeatRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='state', full_name='xain.protobuf.coordinator.HeartbeatRequest.state', index=0,
-      number=1, type=14, cpp_type=8, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='round', full_name='xain.protobuf.coordinator.HeartbeatRequest.round', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=201,
-  serialized_end=283,
-)
-
-
-_HEARTBEATREPLY = _descriptor.Descriptor(
-  name='HeartbeatReply',
-  full_name='xain.protobuf.coordinator.HeartbeatReply',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='state', full_name='xain.protobuf.coordinator.HeartbeatReply.state', index=0,
-      number=1, type=14, cpp_type=8, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='round', full_name='xain.protobuf.coordinator.HeartbeatReply.round', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=285,
-  serialized_end=365,
-)
-
-
-_STARTTRAININGREQUEST = _descriptor.Descriptor(
-  name='StartTrainingRequest',
-  full_name='xain.protobuf.coordinator.StartTrainingRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=367,
-  serialized_end=389,
-)
-
-
-_STARTTRAININGREPLY = _descriptor.Descriptor(
-  name='StartTrainingReply',
-  full_name='xain.protobuf.coordinator.StartTrainingReply',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='weights', full_name='xain.protobuf.coordinator.StartTrainingReply.weights', index=0,
-      number=1, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='epochs', full_name='xain.protobuf.coordinator.StartTrainingReply.epochs', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='epoch_base', full_name='xain.protobuf.coordinator.StartTrainingReply.epoch_base', index=2,
-      number=3, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=391,
-  serialized_end=492,
-)
-
-
-_ENDTRAININGREQUEST_METRICSENTRY = _descriptor.Descriptor(
-  name='MetricsEntry',
-  full_name='xain.protobuf.coordinator.EndTrainingRequest.MetricsEntry',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='key', full_name='xain.protobuf.coordinator.EndTrainingRequest.MetricsEntry.key', index=0,
-      number=1, type=9, cpp_type=9, label=1,
-      has_default_value=False, default_value=_b("").decode('utf-8'),
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='value', full_name='xain.protobuf.coordinator.EndTrainingRequest.MetricsEntry.value', index=1,
-      number=2, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=_b('8\001'),
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=663,
-  serialized_end=764,
-)
-
-_ENDTRAININGREQUEST_METRICS = _descriptor.Descriptor(
-  name='Metrics',
-  full_name='xain.protobuf.coordinator.EndTrainingRequest.Metrics',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='metrics', full_name='xain.protobuf.coordinator.EndTrainingRequest.Metrics.metrics', index=0,
-      number=1, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=766,
-  serialized_end=820,
-)
-
-_ENDTRAININGREQUEST = _descriptor.Descriptor(
-  name='EndTrainingRequest',
-  full_name='xain.protobuf.coordinator.EndTrainingRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='weights', full_name='xain.protobuf.coordinator.EndTrainingRequest.weights', index=0,
-      number=1, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='number_samples', full_name='xain.protobuf.coordinator.EndTrainingRequest.number_samples', index=1,
-      number=2, type=5, cpp_type=1, label=1,
-      has_default_value=False, default_value=0,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-    _descriptor.FieldDescriptor(
-      name='metrics', full_name='xain.protobuf.coordinator.EndTrainingRequest.metrics', index=2,
-      number=3, type=11, cpp_type=10, label=3,
-      has_default_value=False, default_value=[],
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[_ENDTRAININGREQUEST_METRICSENTRY, _ENDTRAININGREQUEST_METRICS, ],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=495,
-  serialized_end=820,
-)
-
-
-_ENDTRAININGREPLY = _descriptor.Descriptor(
-  name='EndTrainingReply',
-  full_name='xain.protobuf.coordinator.EndTrainingReply',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=822,
-  serialized_end=840,
-)
-
-_RENDEZVOUSREPLY.fields_by_name['response'].enum_type = _RENDEZVOUSRESPONSE
-_HEARTBEATREQUEST.fields_by_name['state'].enum_type = _STATE
-_HEARTBEATREPLY.fields_by_name['state'].enum_type = _STATE
-_STARTTRAININGREPLY.fields_by_name['weights'].message_type = numproto_dot_protobuf_dot_ndarray__pb2._NDARRAY
-_ENDTRAININGREQUEST_METRICSENTRY.fields_by_name['value'].message_type = _ENDTRAININGREQUEST_METRICS
-_ENDTRAININGREQUEST_METRICSENTRY.containing_type = _ENDTRAININGREQUEST
-_ENDTRAININGREQUEST_METRICS.fields_by_name['metrics'].message_type = numproto_dot_protobuf_dot_ndarray__pb2._NDARRAY
-_ENDTRAININGREQUEST_METRICS.containing_type = _ENDTRAININGREQUEST
-_ENDTRAININGREQUEST.fields_by_name['weights'].message_type = numproto_dot_protobuf_dot_ndarray__pb2._NDARRAY
-_ENDTRAININGREQUEST.fields_by_name['metrics'].message_type = _ENDTRAININGREQUEST_METRICSENTRY
-DESCRIPTOR.message_types_by_name['RendezvousRequest'] = _RENDEZVOUSREQUEST
-DESCRIPTOR.message_types_by_name['RendezvousReply'] = _RENDEZVOUSREPLY
-DESCRIPTOR.message_types_by_name['HeartbeatRequest'] = _HEARTBEATREQUEST
-DESCRIPTOR.message_types_by_name['HeartbeatReply'] = _HEARTBEATREPLY
-DESCRIPTOR.message_types_by_name['StartTrainingRequest'] = _STARTTRAININGREQUEST
-DESCRIPTOR.message_types_by_name['StartTrainingReply'] = _STARTTRAININGREPLY
-DESCRIPTOR.message_types_by_name['EndTrainingRequest'] = _ENDTRAININGREQUEST
-DESCRIPTOR.message_types_by_name['EndTrainingReply'] = _ENDTRAININGREPLY
-DESCRIPTOR.enum_types_by_name['RendezvousResponse'] = _RENDEZVOUSRESPONSE
-DESCRIPTOR.enum_types_by_name['State'] = _STATE
-_sym_db.RegisterFileDescriptor(DESCRIPTOR)
-
-RendezvousRequest = _reflection.GeneratedProtocolMessageType('RendezvousRequest', (_message.Message,), {
-  'DESCRIPTOR' : _RENDEZVOUSREQUEST,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.RendezvousRequest)
-  })
-_sym_db.RegisterMessage(RendezvousRequest)
-
-RendezvousReply = _reflection.GeneratedProtocolMessageType('RendezvousReply', (_message.Message,), {
-  'DESCRIPTOR' : _RENDEZVOUSREPLY,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.RendezvousReply)
-  })
-_sym_db.RegisterMessage(RendezvousReply)
-
-HeartbeatRequest = _reflection.GeneratedProtocolMessageType('HeartbeatRequest', (_message.Message,), {
-  'DESCRIPTOR' : _HEARTBEATREQUEST,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.HeartbeatRequest)
-  })
-_sym_db.RegisterMessage(HeartbeatRequest)
-
-HeartbeatReply = _reflection.GeneratedProtocolMessageType('HeartbeatReply', (_message.Message,), {
-  'DESCRIPTOR' : _HEARTBEATREPLY,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.HeartbeatReply)
-  })
-_sym_db.RegisterMessage(HeartbeatReply)
-
-StartTrainingRequest = _reflection.GeneratedProtocolMessageType('StartTrainingRequest', (_message.Message,), {
-  'DESCRIPTOR' : _STARTTRAININGREQUEST,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.StartTrainingRequest)
-  })
-_sym_db.RegisterMessage(StartTrainingRequest)
-
-StartTrainingReply = _reflection.GeneratedProtocolMessageType('StartTrainingReply', (_message.Message,), {
-  'DESCRIPTOR' : _STARTTRAININGREPLY,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.StartTrainingReply)
-  })
-_sym_db.RegisterMessage(StartTrainingReply)
-
-EndTrainingRequest = _reflection.GeneratedProtocolMessageType('EndTrainingRequest', (_message.Message,), {
-
-  'MetricsEntry' : _reflection.GeneratedProtocolMessageType('MetricsEntry', (_message.Message,), {
-    'DESCRIPTOR' : _ENDTRAININGREQUEST_METRICSENTRY,
-    '__module__' : 'xain_fl.cproto.coordinator_pb2'
-    # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.EndTrainingRequest.MetricsEntry)
-    })
-  ,
-
-  'Metrics' : _reflection.GeneratedProtocolMessageType('Metrics', (_message.Message,), {
-    'DESCRIPTOR' : _ENDTRAININGREQUEST_METRICS,
-    '__module__' : 'xain_fl.cproto.coordinator_pb2'
-    # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.EndTrainingRequest.Metrics)
-    })
-  ,
-  'DESCRIPTOR' : _ENDTRAININGREQUEST,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.EndTrainingRequest)
-  })
-_sym_db.RegisterMessage(EndTrainingRequest)
-_sym_db.RegisterMessage(EndTrainingRequest.MetricsEntry)
-_sym_db.RegisterMessage(EndTrainingRequest.Metrics)
-
-EndTrainingReply = _reflection.GeneratedProtocolMessageType('EndTrainingReply', (_message.Message,), {
-  'DESCRIPTOR' : _ENDTRAININGREPLY,
-  '__module__' : 'xain_fl.cproto.coordinator_pb2'
-  # @@protoc_insertion_point(class_scope:xain.protobuf.coordinator.EndTrainingReply)
-  })
-_sym_db.RegisterMessage(EndTrainingReply)
-
-
-_ENDTRAININGREQUEST_METRICSENTRY._options = None
-
-_COORDINATOR = _descriptor.ServiceDescriptor(
-  name='Coordinator',
-  full_name='xain.protobuf.coordinator.Coordinator',
-  file=DESCRIPTOR,
-  index=0,
-  serialized_options=None,
-  serialized_start=960,
-  serialized_end=1406,
-  methods=[
-  _descriptor.MethodDescriptor(
-    name='Rendezvous',
-    full_name='xain.protobuf.coordinator.Coordinator.Rendezvous',
-    index=0,
-    containing_service=None,
-    input_type=_RENDEZVOUSREQUEST,
-    output_type=_RENDEZVOUSREPLY,
-    serialized_options=None,
-  ),
-  _descriptor.MethodDescriptor(
-    name='Heartbeat',
-    full_name='xain.protobuf.coordinator.Coordinator.Heartbeat',
-    index=1,
-    containing_service=None,
-    input_type=_HEARTBEATREQUEST,
-    output_type=_HEARTBEATREPLY,
-    serialized_options=None,
-  ),
-  _descriptor.MethodDescriptor(
-    name='StartTraining',
-    full_name='xain.protobuf.coordinator.Coordinator.StartTraining',
-    index=2,
-    containing_service=None,
-    input_type=_STARTTRAININGREQUEST,
-    output_type=_STARTTRAININGREPLY,
-    serialized_options=None,
-  ),
-  _descriptor.MethodDescriptor(
-    name='EndTraining',
-    full_name='xain.protobuf.coordinator.Coordinator.EndTraining',
-    index=3,
-    containing_service=None,
-    input_type=_ENDTRAININGREQUEST,
-    output_type=_ENDTRAININGREPLY,
-    serialized_options=None,
-  ),
-])
-_sym_db.RegisterServiceDescriptor(_COORDINATOR)
-
-DESCRIPTOR.services_by_name['Coordinator'] = _COORDINATOR
-
-# @@protoc_insertion_point(module_scope)
diff --git a/xain_fl/cproto/coordinator_pb2.pyi b/xain_fl/cproto/coordinator_pb2.pyi
deleted file mode 100644
index 248f8541b..000000000
--- a/xain_fl/cproto/coordinator_pb2.pyi
+++ /dev/null
@@ -1,249 +0,0 @@
-# @generated by generate_proto_mypy_stubs.py.  Do not edit!
-import sys
-from google.protobuf.descriptor import (
-    Descriptor as google___protobuf___descriptor___Descriptor,
-    EnumDescriptor as google___protobuf___descriptor___EnumDescriptor,
-)
-
-from google.protobuf.internal.containers import (
-    RepeatedCompositeFieldContainer as google___protobuf___internal___containers___RepeatedCompositeFieldContainer,
-)
-
-from google.protobuf.message import (
-    Message as google___protobuf___message___Message,
-)
-
-from numproto.protobuf.ndarray_pb2 import (
-    NDArray as numproto___protobuf___ndarray_pb2___NDArray,
-)
-
-from typing import (
-    Iterable as typing___Iterable,
-    List as typing___List,
-    Mapping as typing___Mapping,
-    MutableMapping as typing___MutableMapping,
-    Optional as typing___Optional,
-    Text as typing___Text,
-    Tuple as typing___Tuple,
-    cast as typing___cast,
-)
-
-from typing_extensions import (
-    Literal as typing_extensions___Literal,
-)
-
-
-class RendezvousResponse(int):
-    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
-    @classmethod
-    def Name(cls, number: int) -> str: ...
-    @classmethod
-    def Value(cls, name: str) -> RendezvousResponse: ...
-    @classmethod
-    def keys(cls) -> typing___List[str]: ...
-    @classmethod
-    def values(cls) -> typing___List[RendezvousResponse]: ...
-    @classmethod
-    def items(cls) -> typing___List[typing___Tuple[str, RendezvousResponse]]: ...
-    ACCEPT = typing___cast(RendezvousResponse, 0)
-    LATER = typing___cast(RendezvousResponse, 1)
-ACCEPT = typing___cast(RendezvousResponse, 0)
-LATER = typing___cast(RendezvousResponse, 1)
-
-class State(int):
-    DESCRIPTOR: google___protobuf___descriptor___EnumDescriptor = ...
-    @classmethod
-    def Name(cls, number: int) -> str: ...
-    @classmethod
-    def Value(cls, name: str) -> State: ...
-    @classmethod
-    def keys(cls) -> typing___List[str]: ...
-    @classmethod
-    def values(cls) -> typing___List[State]: ...
-    @classmethod
-    def items(cls) -> typing___List[typing___Tuple[str, State]]: ...
-    STANDBY = typing___cast(State, 0)
-    ROUND = typing___cast(State, 1)
-    FINISHED = typing___cast(State, 2)
-    READY = typing___cast(State, 3)
-    TRAINING = typing___cast(State, 4)
-STANDBY = typing___cast(State, 0)
-ROUND = typing___cast(State, 1)
-FINISHED = typing___cast(State, 2)
-READY = typing___cast(State, 3)
-TRAINING = typing___cast(State, 4)
-
-class RendezvousRequest(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-    def __init__(self,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> RendezvousRequest: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-
-class RendezvousReply(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-    response = ... # type: RendezvousResponse
-
-    def __init__(self,
-        *,
-        response : typing___Optional[RendezvousResponse] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> RendezvousReply: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def ClearField(self, field_name: typing_extensions___Literal[u"response"]) -> None: ...
-    else:
-        def ClearField(self, field_name: typing_extensions___Literal[u"response",b"response"]) -> None: ...
-
-class HeartbeatRequest(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-    state = ... # type: State
-    round = ... # type: int
-
-    def __init__(self,
-        *,
-        state : typing___Optional[State] = None,
-        round : typing___Optional[int] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> HeartbeatRequest: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def ClearField(self, field_name: typing_extensions___Literal[u"round",u"state"]) -> None: ...
-    else:
-        def ClearField(self, field_name: typing_extensions___Literal[u"round",b"round",u"state",b"state"]) -> None: ...
-
-class HeartbeatReply(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-    state = ... # type: State
-    round = ... # type: int
-
-    def __init__(self,
-        *,
-        state : typing___Optional[State] = None,
-        round : typing___Optional[int] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> HeartbeatReply: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def ClearField(self, field_name: typing_extensions___Literal[u"round",u"state"]) -> None: ...
-    else:
-        def ClearField(self, field_name: typing_extensions___Literal[u"round",b"round",u"state",b"state"]) -> None: ...
-
-class StartTrainingRequest(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-    def __init__(self,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> StartTrainingRequest: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-
-class StartTrainingReply(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-    epochs = ... # type: int
-    epoch_base = ... # type: int
-
-    @property
-    def weights(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[numproto___protobuf___ndarray_pb2___NDArray]: ...
-
-    def __init__(self,
-        *,
-        weights : typing___Optional[typing___Iterable[numproto___protobuf___ndarray_pb2___NDArray]] = None,
-        epochs : typing___Optional[int] = None,
-        epoch_base : typing___Optional[int] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> StartTrainingReply: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def ClearField(self, field_name: typing_extensions___Literal[u"epoch_base",u"epochs",u"weights"]) -> None: ...
-    else:
-        def ClearField(self, field_name: typing_extensions___Literal[u"epoch_base",b"epoch_base",u"epochs",b"epochs",u"weights",b"weights"]) -> None: ...
-
-class EndTrainingRequest(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-    class MetricsEntry(google___protobuf___message___Message):
-        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-        key = ... # type: typing___Text
-
-        @property
-        def value(self) -> EndTrainingRequest.Metrics: ...
-
-        def __init__(self,
-            *,
-            key : typing___Optional[typing___Text] = None,
-            value : typing___Optional[EndTrainingRequest.Metrics] = None,
-            ) -> None: ...
-        @classmethod
-        def FromString(cls, s: bytes) -> EndTrainingRequest.MetricsEntry: ...
-        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-        if sys.version_info >= (3,):
-            def HasField(self, field_name: typing_extensions___Literal[u"value"]) -> bool: ...
-            def ClearField(self, field_name: typing_extensions___Literal[u"key",u"value"]) -> None: ...
-        else:
-            def HasField(self, field_name: typing_extensions___Literal[u"value",b"value"]) -> bool: ...
-            def ClearField(self, field_name: typing_extensions___Literal[u"key",b"key",u"value",b"value"]) -> None: ...
-
-    class Metrics(google___protobuf___message___Message):
-        DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-        @property
-        def metrics(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[numproto___protobuf___ndarray_pb2___NDArray]: ...
-
-        def __init__(self,
-            *,
-            metrics : typing___Optional[typing___Iterable[numproto___protobuf___ndarray_pb2___NDArray]] = None,
-            ) -> None: ...
-        @classmethod
-        def FromString(cls, s: bytes) -> EndTrainingRequest.Metrics: ...
-        def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-        def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-        if sys.version_info >= (3,):
-            def ClearField(self, field_name: typing_extensions___Literal[u"metrics"]) -> None: ...
-        else:
-            def ClearField(self, field_name: typing_extensions___Literal[u"metrics",b"metrics"]) -> None: ...
-
-    number_samples = ... # type: int
-
-    @property
-    def weights(self) -> google___protobuf___internal___containers___RepeatedCompositeFieldContainer[numproto___protobuf___ndarray_pb2___NDArray]: ...
-
-    @property
-    def metrics(self) -> typing___MutableMapping[typing___Text, EndTrainingRequest.Metrics]: ...
-
-    def __init__(self,
-        *,
-        weights : typing___Optional[typing___Iterable[numproto___protobuf___ndarray_pb2___NDArray]] = None,
-        number_samples : typing___Optional[int] = None,
-        metrics : typing___Optional[typing___Mapping[typing___Text, EndTrainingRequest.Metrics]] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> EndTrainingRequest: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def ClearField(self, field_name: typing_extensions___Literal[u"metrics",u"number_samples",u"weights"]) -> None: ...
-    else:
-        def ClearField(self, field_name: typing_extensions___Literal[u"metrics",b"metrics",u"number_samples",b"number_samples",u"weights",b"weights"]) -> None: ...
-
-class EndTrainingReply(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-    def __init__(self,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> EndTrainingReply: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
diff --git a/xain_fl/cproto/coordinator_pb2_grpc.py b/xain_fl/cproto/coordinator_pb2_grpc.py
deleted file mode 100644
index f2330c124..000000000
--- a/xain_fl/cproto/coordinator_pb2_grpc.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
-import grpc
-
-from xain_fl.cproto import coordinator_pb2 as xain__fl_dot_cproto_dot_coordinator__pb2
-
-
-class CoordinatorStub(object):
-  # missing associated documentation comment in .proto file
-  pass
-
-  def __init__(self, channel):
-    """Constructor.
-
-    Args:
-      channel: A grpc.Channel.
-    """
-    self.Rendezvous = channel.unary_unary(
-        '/xain.protobuf.coordinator.Coordinator/Rendezvous',
-        request_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.RendezvousRequest.SerializeToString,
-        response_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.RendezvousReply.FromString,
-        )
-    self.Heartbeat = channel.unary_unary(
-        '/xain.protobuf.coordinator.Coordinator/Heartbeat',
-        request_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.HeartbeatRequest.SerializeToString,
-        response_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.HeartbeatReply.FromString,
-        )
-    self.StartTraining = channel.unary_unary(
-        '/xain.protobuf.coordinator.Coordinator/StartTraining',
-        request_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.StartTrainingRequest.SerializeToString,
-        response_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.StartTrainingReply.FromString,
-        )
-    self.EndTraining = channel.unary_unary(
-        '/xain.protobuf.coordinator.Coordinator/EndTraining',
-        request_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.EndTrainingRequest.SerializeToString,
-        response_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.EndTrainingReply.FromString,
-        )
-
-
-class CoordinatorServicer(object):
-  # missing associated documentation comment in .proto file
-  pass
-
-  def Rendezvous(self, request, context):
-    # missing associated documentation comment in .proto file
-    pass
-    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-    context.set_details('Method not implemented!')
-    raise NotImplementedError('Method not implemented!')
-
-  def Heartbeat(self, request, context):
-    # missing associated documentation comment in .proto file
-    pass
-    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-    context.set_details('Method not implemented!')
-    raise NotImplementedError('Method not implemented!')
-
-  def StartTraining(self, request, context):
-    # missing associated documentation comment in .proto file
-    pass
-    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-    context.set_details('Method not implemented!')
-    raise NotImplementedError('Method not implemented!')
-
-  def EndTraining(self, request, context):
-    # missing associated documentation comment in .proto file
-    pass
-    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-    context.set_details('Method not implemented!')
-    raise NotImplementedError('Method not implemented!')
-
-
-def add_CoordinatorServicer_to_server(servicer, server):
-  rpc_method_handlers = {
-      'Rendezvous': grpc.unary_unary_rpc_method_handler(
-          servicer.Rendezvous,
-          request_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.RendezvousRequest.FromString,
-          response_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.RendezvousReply.SerializeToString,
-      ),
-      'Heartbeat': grpc.unary_unary_rpc_method_handler(
-          servicer.Heartbeat,
-          request_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.HeartbeatRequest.FromString,
-          response_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.HeartbeatReply.SerializeToString,
-      ),
-      'StartTraining': grpc.unary_unary_rpc_method_handler(
-          servicer.StartTraining,
-          request_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.StartTrainingRequest.FromString,
-          response_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.StartTrainingReply.SerializeToString,
-      ),
-      'EndTraining': grpc.unary_unary_rpc_method_handler(
-          servicer.EndTraining,
-          request_deserializer=xain__fl_dot_cproto_dot_coordinator__pb2.EndTrainingRequest.FromString,
-          response_serializer=xain__fl_dot_cproto_dot_coordinator__pb2.EndTrainingReply.SerializeToString,
-      ),
-  }
-  generic_handler = grpc.method_handlers_generic_handler(
-      'xain.protobuf.coordinator.Coordinator', rpc_method_handlers)
-  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/xain_fl/cproto/hellonumproto_pb2.py b/xain_fl/cproto/hellonumproto_pb2.py
deleted file mode 100644
index 931fa62eb..000000000
--- a/xain_fl/cproto/hellonumproto_pb2.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# -*- coding: utf-8 -*-
-# Generated by the protocol buffer compiler.  DO NOT EDIT!
-# source: xain_fl/cproto/hellonumproto.proto
-
-import sys
-_b=sys.version_info[0]<3 and (lambda x:x) or (lambda x:x.encode('latin1'))
-from google.protobuf import descriptor as _descriptor
-from google.protobuf import message as _message
-from google.protobuf import reflection as _reflection
-from google.protobuf import symbol_database as _symbol_database
-# @@protoc_insertion_point(imports)
-
-_sym_db = _symbol_database.Default()
-
-
-from numproto.protobuf import ndarray_pb2 as numproto_dot_protobuf_dot_ndarray__pb2
-
-
-DESCRIPTOR = _descriptor.FileDescriptor(
-  name='xain_fl/cproto/hellonumproto.proto',
-  package='hellonumproto',
-  syntax='proto3',
-  serialized_options=None,
-  serialized_pb=_b('\n\"xain_fl/cproto/hellonumproto.proto\x12\rhellonumproto\x1a\x1fnumproto/protobuf/ndarray.proto\":\n\x0fNumProtoRequest\x12\'\n\x03\x61rr\x18\x01 \x01(\x0b\x32\x1a.numproto.protobuf.NDArray\"8\n\rNumProtoReply\x12\'\n\x03\x61rr\x18\x01 \x01(\x0b\x32\x1a.numproto.protobuf.NDArray2d\n\x0eNumProtoServer\x12R\n\x10SayHelloNumProto\x12\x1e.hellonumproto.NumProtoRequest\x1a\x1c.hellonumproto.NumProtoReply\"\x00\x62\x06proto3')
-  ,
-  dependencies=[numproto_dot_protobuf_dot_ndarray__pb2.DESCRIPTOR,])
-
-
-
-
-_NUMPROTOREQUEST = _descriptor.Descriptor(
-  name='NumProtoRequest',
-  full_name='hellonumproto.NumProtoRequest',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='arr', full_name='hellonumproto.NumProtoRequest.arr', index=0,
-      number=1, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=86,
-  serialized_end=144,
-)
-
-
-_NUMPROTOREPLY = _descriptor.Descriptor(
-  name='NumProtoReply',
-  full_name='hellonumproto.NumProtoReply',
-  filename=None,
-  file=DESCRIPTOR,
-  containing_type=None,
-  fields=[
-    _descriptor.FieldDescriptor(
-      name='arr', full_name='hellonumproto.NumProtoReply.arr', index=0,
-      number=1, type=11, cpp_type=10, label=1,
-      has_default_value=False, default_value=None,
-      message_type=None, enum_type=None, containing_type=None,
-      is_extension=False, extension_scope=None,
-      serialized_options=None, file=DESCRIPTOR),
-  ],
-  extensions=[
-  ],
-  nested_types=[],
-  enum_types=[
-  ],
-  serialized_options=None,
-  is_extendable=False,
-  syntax='proto3',
-  extension_ranges=[],
-  oneofs=[
-  ],
-  serialized_start=146,
-  serialized_end=202,
-)
-
-_NUMPROTOREQUEST.fields_by_name['arr'].message_type = numproto_dot_protobuf_dot_ndarray__pb2._NDARRAY
-_NUMPROTOREPLY.fields_by_name['arr'].message_type = numproto_dot_protobuf_dot_ndarray__pb2._NDARRAY
-DESCRIPTOR.message_types_by_name['NumProtoRequest'] = _NUMPROTOREQUEST
-DESCRIPTOR.message_types_by_name['NumProtoReply'] = _NUMPROTOREPLY
-_sym_db.RegisterFileDescriptor(DESCRIPTOR)
-
-NumProtoRequest = _reflection.GeneratedProtocolMessageType('NumProtoRequest', (_message.Message,), {
-  'DESCRIPTOR' : _NUMPROTOREQUEST,
-  '__module__' : 'xain_fl.cproto.hellonumproto_pb2'
-  # @@protoc_insertion_point(class_scope:hellonumproto.NumProtoRequest)
-  })
-_sym_db.RegisterMessage(NumProtoRequest)
-
-NumProtoReply = _reflection.GeneratedProtocolMessageType('NumProtoReply', (_message.Message,), {
-  'DESCRIPTOR' : _NUMPROTOREPLY,
-  '__module__' : 'xain_fl.cproto.hellonumproto_pb2'
-  # @@protoc_insertion_point(class_scope:hellonumproto.NumProtoReply)
-  })
-_sym_db.RegisterMessage(NumProtoReply)
-
-
-
-_NUMPROTOSERVER = _descriptor.ServiceDescriptor(
-  name='NumProtoServer',
-  full_name='hellonumproto.NumProtoServer',
-  file=DESCRIPTOR,
-  index=0,
-  serialized_options=None,
-  serialized_start=204,
-  serialized_end=304,
-  methods=[
-  _descriptor.MethodDescriptor(
-    name='SayHelloNumProto',
-    full_name='hellonumproto.NumProtoServer.SayHelloNumProto',
-    index=0,
-    containing_service=None,
-    input_type=_NUMPROTOREQUEST,
-    output_type=_NUMPROTOREPLY,
-    serialized_options=None,
-  ),
-])
-_sym_db.RegisterServiceDescriptor(_NUMPROTOSERVER)
-
-DESCRIPTOR.services_by_name['NumProtoServer'] = _NUMPROTOSERVER
-
-# @@protoc_insertion_point(module_scope)
diff --git a/xain_fl/cproto/hellonumproto_pb2.pyi b/xain_fl/cproto/hellonumproto_pb2.pyi
deleted file mode 100644
index 35ec44890..000000000
--- a/xain_fl/cproto/hellonumproto_pb2.pyi
+++ /dev/null
@@ -1,64 +0,0 @@
-# @generated by generate_proto_mypy_stubs.py.  Do not edit!
-import sys
-from google.protobuf.descriptor import (
-    Descriptor as google___protobuf___descriptor___Descriptor,
-)
-
-from google.protobuf.message import (
-    Message as google___protobuf___message___Message,
-)
-
-from numproto.protobuf.ndarray_pb2 import (
-    NDArray as numproto___protobuf___ndarray_pb2___NDArray,
-)
-
-from typing import (
-    Optional as typing___Optional,
-)
-
-from typing_extensions import (
-    Literal as typing_extensions___Literal,
-)
-
-
-class NumProtoRequest(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-    @property
-    def arr(self) -> numproto___protobuf___ndarray_pb2___NDArray: ...
-
-    def __init__(self,
-        *,
-        arr : typing___Optional[numproto___protobuf___ndarray_pb2___NDArray] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> NumProtoRequest: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def HasField(self, field_name: typing_extensions___Literal[u"arr"]) -> bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"arr"]) -> None: ...
-    else:
-        def HasField(self, field_name: typing_extensions___Literal[u"arr",b"arr"]) -> bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"arr",b"arr"]) -> None: ...
-
-class NumProtoReply(google___protobuf___message___Message):
-    DESCRIPTOR: google___protobuf___descriptor___Descriptor = ...
-
-    @property
-    def arr(self) -> numproto___protobuf___ndarray_pb2___NDArray: ...
-
-    def __init__(self,
-        *,
-        arr : typing___Optional[numproto___protobuf___ndarray_pb2___NDArray] = None,
-        ) -> None: ...
-    @classmethod
-    def FromString(cls, s: bytes) -> NumProtoReply: ...
-    def MergeFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    def CopyFrom(self, other_msg: google___protobuf___message___Message) -> None: ...
-    if sys.version_info >= (3,):
-        def HasField(self, field_name: typing_extensions___Literal[u"arr"]) -> bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"arr"]) -> None: ...
-    else:
-        def HasField(self, field_name: typing_extensions___Literal[u"arr",b"arr"]) -> bool: ...
-        def ClearField(self, field_name: typing_extensions___Literal[u"arr",b"arr"]) -> None: ...
diff --git a/xain_fl/cproto/hellonumproto_pb2_grpc.py b/xain_fl/cproto/hellonumproto_pb2_grpc.py
deleted file mode 100644
index 8695042c5..000000000
--- a/xain_fl/cproto/hellonumproto_pb2_grpc.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# Generated by the gRPC Python protocol compiler plugin. DO NOT EDIT!
-import grpc
-
-from xain_fl.cproto import hellonumproto_pb2 as xain__fl_dot_cproto_dot_hellonumproto__pb2
-
-
-class NumProtoServerStub(object):
-  # missing associated documentation comment in .proto file
-  pass
-
-  def __init__(self, channel):
-    """Constructor.
-
-    Args:
-      channel: A grpc.Channel.
-    """
-    self.SayHelloNumProto = channel.unary_unary(
-        '/hellonumproto.NumProtoServer/SayHelloNumProto',
-        request_serializer=xain__fl_dot_cproto_dot_hellonumproto__pb2.NumProtoRequest.SerializeToString,
-        response_deserializer=xain__fl_dot_cproto_dot_hellonumproto__pb2.NumProtoReply.FromString,
-        )
-
-
-class NumProtoServerServicer(object):
-  # missing associated documentation comment in .proto file
-  pass
-
-  def SayHelloNumProto(self, request, context):
-    # missing associated documentation comment in .proto file
-    pass
-    context.set_code(grpc.StatusCode.UNIMPLEMENTED)
-    context.set_details('Method not implemented!')
-    raise NotImplementedError('Method not implemented!')
-
-
-def add_NumProtoServerServicer_to_server(servicer, server):
-  rpc_method_handlers = {
-      'SayHelloNumProto': grpc.unary_unary_rpc_method_handler(
-          servicer.SayHelloNumProto,
-          request_deserializer=xain__fl_dot_cproto_dot_hellonumproto__pb2.NumProtoRequest.FromString,
-          response_serializer=xain__fl_dot_cproto_dot_hellonumproto__pb2.NumProtoReply.SerializeToString,
-      ),
-  }
-  generic_handler = grpc.method_handlers_generic_handler(
-      'hellonumproto.NumProtoServer', rpc_method_handlers)
-  server.add_generic_rpc_handlers((generic_handler,))
diff --git a/xain_fl/cproto/numproto_server.py b/xain_fl/cproto/numproto_server.py
deleted file mode 100644
index 9ce77f9d0..000000000
--- a/xain_fl/cproto/numproto_server.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import time
-from concurrent import futures
-
-import grpc
-from numproto import ndarray_to_proto, proto_to_ndarray
-
-from xain_fl.coordinator import _ONE_DAY_IN_SECONDS
-from xain_fl.cproto import hellonumproto_pb2, hellonumproto_pb2_grpc
-from xain_fl.logger import get_logger
-
-logger = get_logger(__name__)
-
-
-class NumProtoServer(hellonumproto_pb2_grpc.NumProtoServerServicer):
-    def SayHelloNumProto(self, request, context):
-        nda = proto_to_ndarray(request.arr)
-        logger.info("NumProto server received", nda=nda)
-
-        nda *= 2
-        logger.info("NumProto server sent", nda=nda)
-        return hellonumproto_pb2.NumProtoReply(arr=ndarray_to_proto(nda))
-
-
-def serve():
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
-    hellonumproto_pb2_grpc.add_NumProtoServerServicer_to_server(
-        NumProtoServer(), server
-    )
-    server.add_insecure_port("[::]:50051")
-    server.start()
-    try:
-        while True:
-            time.sleep(_ONE_DAY_IN_SECONDS)
-    except KeyboardInterrupt:
-        server.stop(0)
-
-
-if __name__ == "__main__":
-    serve()
diff --git a/xain_fl/cproto/participant.py b/xain_fl/cproto/participant.py
deleted file mode 100644
index cbfcc50bf..000000000
--- a/xain_fl/cproto/participant.py
+++ /dev/null
@@ -1,304 +0,0 @@
-"""Module implementing the networked Participant using gRPC.
-"""
-
-# TODO: Currently, the participant is completely ignored in the mypy.ini, since the participant
-# will be removed anyways in XP-208.
-
-import threading
-import time
-from enum import Enum, auto
-from typing import Tuple
-
-import grpc
-from numproto import ndarray_to_proto, proto_to_ndarray
-
-from xain_fl.cproto import coordinator_pb2, coordinator_pb2_grpc
-from xain_fl.logger import get_logger
-from xain_fl.types import History, Metrics, Theta
-
-RETRY_TIMEOUT = 5
-HEARTBEAT_TIME = 10
-
-logger = get_logger(__name__)
-
-
-class ParState(Enum):
-    """Enumeration of Participant states.
-    """
-
-    WAITING_FOR_SELECTION = auto()
-    TRAINING = auto()
-    POST_TRAINING = auto()
-    DONE = auto()
-
-
-def rendezvous(channel):
-    """Starts a rendezvous exchange with Coordinator.
-
-    Args:
-        channel: gRPC channel to Coordinator.
-    """
-    stub = coordinator_pb2_grpc.CoordinatorStub(channel)
-
-    response = coordinator_pb2.RendezvousResponse.LATER
-
-    while response == coordinator_pb2.RendezvousResponse.LATER:
-        reply = stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-        if reply.response == coordinator_pb2.RendezvousResponse.ACCEPT:
-            logger.info("Participant received: ACCEPT")
-        elif reply.response == coordinator_pb2.RendezvousResponse.LATER:
-            logger.info(
-                "Participant received: LATER. Retrying...", retry_timeout=RETRY_TIMEOUT
-            )
-            time.sleep(RETRY_TIMEOUT)
-
-        response = reply.response
-
-
-def start_training(channel) -> Tuple[Theta, int, int]:
-    """Starts a training initiation exchange with Coordinator. Returns the decoded
-    contents of the response from Coordinator.
-
-    Args:
-        channel: gRPC channel to Coordinator.
-
-    Returns:
-        obj:`Theta`: Global model to train on.
-        obj:`int`: Number of epochs.
-        obj:`int`: Epoch base.
-    """
-    stub = coordinator_pb2_grpc.CoordinatorStub(channel)
-    req = coordinator_pb2.StartTrainingRequest()
-    # send request to start training
-    reply = stub.StartTraining(req)
-    logger.info("Participant received", reply_type=type(reply))
-    theta, epochs, epoch_base = reply.theta, reply.epochs, reply.epoch_base
-    return [proto_to_ndarray(pnda) for pnda in theta], epochs, epoch_base
-
-
-def end_training(
-    channel, theta_n: Tuple[Theta, int], history: History, metrics: Metrics
-):
-    """Starts a training completion exchange with Coordinator, sending a locally
-    trained model and metadata.
-
-    Args:
-        channel: gRPC channel to Coordinator.
-        theta_n (obj:`Tuple[Theta, int]`): Locally trained model.
-        history (obj:`History`): History metadata.
-        Metrics (obj:`Metrics`): Metrics metadata.
-    """
-
-    # pylint: disable=no-member
-    stub = coordinator_pb2_grpc.CoordinatorStub(channel)
-    # build request starting with theta update
-    theta, num = theta_n
-    theta_n_proto = coordinator_pb2.EndTrainingRequest.ThetaUpdate(
-        theta_prime=[ndarray_to_proto(nda) for nda in theta], num_examples=num
-    )
-    # history data
-    h = {
-        k: coordinator_pb2.EndTrainingRequest.HistoryValue(values=v)
-        for k, v in history.items()
-    }
-    # metrics
-    cid, vbc = metrics
-    m = coordinator_pb2.EndTrainingRequest.Metrics(cid=cid, vol_by_class=vbc)
-    # assemble req
-    req = coordinator_pb2.EndTrainingRequest(
-        theta_update=theta_n_proto, history=h, metrics=m
-    )
-    # send request to end training
-    reply = stub.EndTraining(req)
-    logger.info("Participant received", reply_type=type(reply))
-
-
-def training_round(channel, participant):
-    """Initiates training round exchange with Coordinator.
-    Begins with `start_training`. Then performs local training computation using
-    `participant`. Finally, completes with `end_training`.
-
-    Args:
-        channel: gRPC channel to Coordinator.
-        participant (obj:`Participant`): Local Participant.
-    """
-    theta, epochs, base = start_training(channel)
-    # training:
-    theta_n, his, _dict = participant.train_round(theta, epochs, base)
-    # NOTE _dict is the opt_config - ignore for now
-    met = participant.metrics()
-    end_training(channel, theta_n, his, met)
-
-
-class StateRecord:
-    """Thread-safe record of Participant state and round number.
-    """
-
-    # pylint: disable=W0622
-    def __init__(self, state=ParState.WAITING_FOR_SELECTION, round=0):
-        self.cv = threading.Condition()
-        self.round = round
-        self.state = state
-
-    def lookup(self):
-        """Looks up the state and round number.
-
-        Returns:
-            :obj:`Tuple[ParState, int]`: State and round number
-        """
-        with self.cv:
-            return self.state, self.round
-
-    def update(self, state):
-        """Updates state.
-
-        Args:
-            state (:obj:`ParState`): State to update to.
-        """
-        with self.cv:
-            self.state = state
-            self.cv.notify()
-
-    def wait_until_selected_or_done(self):
-        """Waits until Participant is in the state of having been selected for training
-        (or is completely done).
-
-        Returns:
-            :obj:`ParState`: New state Participant is in.
-        """
-        with self.cv:
-            self.cv.wait_for(lambda: self.state in {ParState.TRAINING, ParState.DONE})
-            # which one was it?
-            return self.state
-
-    def wait_until_next_round(self):
-        """Waits until Participant is in a state indicating the start of the next round
-        of training.
-
-        Returns:
-            :obj:`ParState`: New state Participant is in.
-        """
-        with self.cv:
-            self.cv.wait_for(
-                lambda: self.state
-                in {ParState.TRAINING, ParState.WAITING_FOR_SELECTION, ParState.DONE}
-            )
-            # which one was it?
-            return self.state
-
-
-def transit(st, beat_reply):
-    """Participant state transition function on a heartbeat response. Updates the
-    state record `st`.
-
-    Args:
-        st (obj:`StateRecord`): Participant state record to update.
-        beat_reply (obj:`coordinator_pb2.HeartbeatReply`): Heartbeat from Coordinator.
-    """
-    msg, r = beat_reply.state, beat_reply.round
-    with st.cv:
-        if st.state == ParState.WAITING_FOR_SELECTION:
-            if msg == coordinator_pb2.State.ROUND:
-                st.state = ParState.TRAINING
-                st.round = r
-                st.cv.notify()
-            elif msg == coordinator_pb2.State.FINISHED:
-                st.state = ParState.DONE
-                st.cv.notify()
-        elif st.state == ParState.POST_TRAINING:
-            if msg == coordinator_pb2.State.STANDBY:
-                # not selected
-                st.state = ParState.WAITING_FOR_SELECTION
-                # prob ok to keep st.round as it is
-                st.cv.notify()
-            elif msg == coordinator_pb2.State.ROUND and r == st.round + 1:
-                st.state = ParState.TRAINING
-                st.round = r
-                st.cv.notify()
-            elif msg == coordinator_pb2.State.FINISHED:
-                st.state = ParState.DONE
-                st.cv.notify()
-
-
-def message_loop(chan, st, terminate):
-    """Periodically sends (and handles) heartbeat messages in a loop.
-
-    Args:
-        chan: gRPC channel to Coordinator.
-        st (obj:`StateRecord`): Participant state record.
-        terminate (obj:`threading.Event`): Event to terminate message loop.
-    """
-    coord = coordinator_pb2_grpc.CoordinatorStub(chan)
-    while not terminate.is_set():
-        req = coordinator_pb2.HeartbeatRequest()
-        reply = coord.Heartbeat(req)
-        transit(st, reply)
-        time.sleep(HEARTBEAT_TIME)
-
-
-def go(part):
-    """Top-level function for the Participant state machine.
-    After rendezvous and heartbeat initiation, the Participant is
-    WAITING_FOR_SELECTION. When selected, it moves to TRAINING followed by
-    POST_TRAINING. If selected again for the next round, it moves back to
-    TRAINING, otherwise it is back to WAITING_FOR_SELECTION.
-
-    Args:
-        part (obj:`Participant`): Participant object for training computation.
-    """
-    # use insecure channel for now
-    with grpc.insecure_channel("localhost:50051") as chan:  # thread-safe
-        rendezvous(chan)
-
-        st = StateRecord()
-        terminate = threading.Event()
-        ml = threading.Thread(target=message_loop, args=(chan, st, terminate))
-        ml.start()
-
-        # in WAITING_FOR_SELECTION state
-        begin_selection_wait(st, chan, part)
-
-        # possibly several training rounds later...
-        # in DONE state
-        terminate.set()
-        ml.join()
-
-
-def begin_selection_wait(st, chan, part):
-    """Perform actions in Participant state WAITING_FOR_SELECTION.
-
-    Args:
-        st (obj:`StateRecord`): Participant state record.
-        chan: gRPC channel to Coordinator.
-        part (obj:`Participant`): Participant object for training computation.
-    """
-    ps = st.wait_until_selected_or_done()
-    if ps == ParState.TRAINING:
-        # selected
-        begin_training(st, chan, part)
-    elif ps == ParState.DONE:
-        pass
-
-
-def begin_training(st, chan, part):
-    """Perform actions in Participant state TRAINING and POST_TRAINING.
-
-    Args:
-        st (obj:`StateRecord`): Participant state record.
-        chan: gRPC channel to Coordinator.
-        part (obj:`Participant`): Participant object for training computation.
-    """
-    # perform the training procedures
-    training_round(chan, part)
-    # move to POST_TRAINING state
-    st.update(ParState.POST_TRAINING)
-    ps = st.wait_until_next_round()
-    if ps == ParState.TRAINING:
-        # selected again
-        begin_training(st, chan, part)
-    elif ps == ParState.WAITING_FOR_SELECTION:
-        # not this time
-        begin_selection_wait(st, chan, part)
-    elif ps == ParState.DONE:
-        # that was the last round
-        pass
diff --git a/xain_fl/cproto/test_coordinator.py b/xain_fl/cproto/test_coordinator.py
deleted file mode 100644
index 49bc54901..000000000
--- a/xain_fl/cproto/test_coordinator.py
+++ /dev/null
@@ -1,267 +0,0 @@
-import numpy as np
-import pytest
-from numproto import proto_to_ndarray
-
-from xain_fl.coordinator.coordinator import Coordinator
-from xain_fl.cproto import coordinator_pb2
-from xain_fl.cproto.coordinator_pb2 import (
-    RendezvousReply,
-    RendezvousRequest,
-    RendezvousResponse,
-)
-from xain_fl.tools.exceptions import (
-    DuplicatedUpdateError,
-    InvalidRequestError,
-    UnknownParticipantError,
-)
-
-
-def test_rendezvous_accept():
-    coordinator: Coordinator = Coordinator()
-    result: RendezvousReply = coordinator.on_message(
-        RendezvousRequest(), "participant1"
-    )
-
-    assert isinstance(result, RendezvousReply)
-    assert result.response == RendezvousResponse.ACCEPT
-
-
-def test_rendezvous_later_fraction_1():
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    result = coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    assert isinstance(result, coordinator_pb2.RendezvousReply)
-    assert result.response == coordinator_pb2.RendezvousResponse.LATER
-
-
-def test_rendezvous_later_fraction_05():
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=0.5
-    )
-
-    # with 0.5 fraction it needs to accept at least two participants
-    result = coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    assert isinstance(result, coordinator_pb2.RendezvousReply)
-    assert result.response == coordinator_pb2.RendezvousResponse.ACCEPT
-
-    result = coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    assert isinstance(result, coordinator_pb2.RendezvousReply)
-    assert result.response == coordinator_pb2.RendezvousResponse.ACCEPT
-
-    # the third participant must receive LATER RendezvousResponse
-    result = coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant3")
-
-    assert isinstance(result, coordinator_pb2.RendezvousReply)
-    assert result.response == coordinator_pb2.RendezvousResponse.LATER
-
-
-def test_coordinator_state_standby_round():
-    # tests that the coordinator transitions from STANDBY to ROUND once enough participants
-    # are connected
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=1.0
-    )
-
-    assert coordinator.state == coordinator_pb2.STANDBY
-
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    assert coordinator.state == coordinator_pb2.State.ROUND
-    assert coordinator.current_round == 1
-
-
-def test_start_training():
-    test_weights = [np.arange(10), np.arange(10, 20)]
-    coordinator = Coordinator(
-        minimum_participants_in_round=1,
-        fraction_of_participants=1.0,
-        weights=test_weights,
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    result = coordinator.on_message(
-        coordinator_pb2.StartTrainingRequest(), "participant1"
-    )
-    received_weights = [proto_to_ndarray(nda) for nda in result.weights]
-
-    np.testing.assert_equal(test_weights, received_weights)
-
-
-def start_training_wrong_state():
-    # if the coordinator receives a StartTraining request while not in the
-    # ROUND state it will raise an exception
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    with pytest.raises(InvalidRequestError):
-        coordinator.on_message(coordinator_pb2.StartTrainingRequest(), "participant1")
-
-
-def test_end_training():
-    # we need two participants so that we can check the status of the local update mid round
-    # with only one participant it wouldn't work because the local updates state is cleaned at
-    # the end of each round
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-
-    assert len(coordinator.round.updates) == 1
-
-
-def test_end_training_round_update():
-    # Test that the round number is updated once all participants sent their updates
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=1.0, num_rounds=2
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    # check that we are currently in round 1
-    assert coordinator.current_round == 1
-
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-    # check we are still in round 1
-    assert coordinator.current_round == 1
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant2")
-
-    # check that round number was updated
-    assert coordinator.current_round == 2
-
-
-def test_end_training_reinitialize_local_models():
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=1.0, num_rounds=2
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-
-    # After one participant sends its updates we should have one update in the coordinator
-    assert len(coordinator.round.updates) == 1
-
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant2")
-
-    # once the second participant delivers its updates the round ends and the local models
-    # are reinitialized
-    assert coordinator.round.updates == {}
-
-
-def test_training_finished():
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=1.0, num_rounds=2
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    # Deliver results for 2 rounds
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-
-    assert coordinator.state == coordinator_pb2.State.FINISHED
-
-
-def test_wrong_participant():
-    # coordinator should not accept requests from participants that it has not accepted
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    with pytest.raises(UnknownParticipantError):
-        coordinator.on_message(coordinator_pb2.HeartbeatRequest(), "participant2")
-
-    with pytest.raises(UnknownParticipantError):
-        coordinator.on_message(coordinator_pb2.StartTrainingRequest(), "participant2")
-
-    with pytest.raises(UnknownParticipantError):
-        coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant2")
-
-
-def test_duplicated_update_submit():
-    # the coordinator should not accept multiples updates from the same participant
-    # in the same round
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-
-    with pytest.raises(DuplicatedUpdateError):
-        coordinator.on_message(coordinator_pb2.EndTrainingRequest(), "participant1")
-
-
-def test_remove_participant():
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=1.0
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    assert coordinator.state == coordinator_pb2.State.ROUND
-
-    coordinator.remove_participant("participant1")
-
-    assert coordinator.participants.len() == 0
-    assert coordinator.state == coordinator_pb2.State.STANDBY
-
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    assert coordinator.state == coordinator_pb2.State.ROUND
-
-
-def test_number_of_selected_participants():
-    # test that the coordinator needs minimum 3 participants and selects 2 of them
-    coordinator = Coordinator(
-        minimum_participants_in_round=2, fraction_of_participants=0.6
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-
-    # the coordinator should wait for three participants to be connected before starting a round,
-    # and select participants. Before that coordinator.round.participant_ids is an empty list
-    assert coordinator.minimum_connected_participants == 3
-    assert coordinator.state == coordinator_pb2.State.STANDBY
-    assert coordinator.round.participant_ids == []
-
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    assert coordinator.state == coordinator_pb2.State.STANDBY
-    assert coordinator.round.participant_ids == []
-
-    # add the third participant
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant3")
-
-    # now the coordinator must have started a round and selected 2 participants
-    assert coordinator.state == coordinator_pb2.State.ROUND
-    assert len(coordinator.round.participant_ids) == 2
-
-
-def test_correct_round_advertised_to_participants():
-    # test that only selected participants receive ROUND state and the others STANDBY
-    coordinator = Coordinator(
-        minimum_participants_in_round=1, fraction_of_participants=0.5
-    )
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant1")
-    coordinator.on_message(coordinator_pb2.RendezvousRequest(), "participant2")
-
-    # override selected participant
-    coordinator.round.participant_ids = ["participant1"]
-
-    # state ROUND will be advertised to participant1 (which has been selected)
-    result = coordinator.on_message(coordinator_pb2.HeartbeatRequest(), "participant1")
-    assert result.state == coordinator_pb2.State.ROUND
-
-    # state STANDBY will be advertised to participant2 (which has NOT been selected)
-    result = coordinator.on_message(coordinator_pb2.HeartbeatRequest(), "participant2")
-    assert result.state == coordinator_pb2.State.STANDBY
diff --git a/xain_fl/cproto/test_grpc.py b/xain_fl/cproto/test_grpc.py
deleted file mode 100644
index c245a7c85..000000000
--- a/xain_fl/cproto/test_grpc.py
+++ /dev/null
@@ -1,257 +0,0 @@
-# TODO: Once the old grpc participant is removed (XP-208), make sure to remove the
-# skipping of tests.
-
-import sys
-import threading
-from concurrent import futures
-from unittest import mock
-
-import grpc
-import numpy as np
-import pytest
-from numproto import ndarray_to_proto, proto_to_ndarray
-
-from xain_fl.coordinator.coordinator import Coordinator
-from xain_fl.coordinator.coordinator_grpc import CoordinatorGrpc
-from xain_fl.coordinator.heartbeat import monitor_heartbeats
-from xain_fl.coordinator.participants import Participants
-from xain_fl.cproto import (
-    coordinator_pb2,
-    coordinator_pb2_grpc,
-    hellonumproto_pb2,
-    hellonumproto_pb2_grpc,
-)
-from xain_fl.cproto.participant import (
-    StateRecord,
-    end_training,
-    message_loop,
-    rendezvous,
-    start_training,
-)
-
-# Some grpc tests fail on macos.
-# `pytestmark` when defined on a module will mark all tests in that module.
-# For more information check
-# http://doc.pytest.org/en/latest/skipping.html#skip-all-test-functions-of-a-class-or-module
-if sys.platform == "darwin":
-    pytestmark = pytest.mark.xfail(reason="some grpc tests fail on macos")
-
-# pylint: disable=W0613,W0621
-@pytest.mark.integration
-def test_greeter_server(greeter_server):
-    with grpc.insecure_channel("localhost:50051") as channel:
-        stub = hellonumproto_pb2_grpc.NumProtoServerStub(channel)
-
-        nda = np.arange(10)
-        response = stub.SayHelloNumProto(
-            hellonumproto_pb2.NumProtoRequest(arr=ndarray_to_proto(nda))
-        )
-
-        response_nda = proto_to_ndarray(response.arr)
-
-        assert np.array_equal(nda * 2, response_nda)
-
-
-@pytest.mark.integration
-def test_participant_rendezvous_accept(participant_stub, coordinator_service):
-    reply = participant_stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-
-    assert reply.response == coordinator_pb2.RendezvousResponse.ACCEPT
-
-
-# TODO(XP-119): Fix test so it also runs correctly on macos
-@pytest.mark.integration
-def test_participant_rendezvous_later(participant_stub):
-
-    # populate participants
-    coordinator = Coordinator(
-        minimum_participants_in_round=10, fraction_of_participants=1.0
-    )
-    required_participants = 10
-    for i in range(required_participants):
-        coordinator.participants.add(str(i))
-
-    server = grpc.server(futures.ThreadPoolExecutor(max_workers=1))
-    coordinator_pb2_grpc.add_CoordinatorServicer_to_server(
-        CoordinatorGrpc(coordinator), server
-    )
-    server.add_insecure_port("localhost:50051")
-    server.start()
-
-    # try to rendezvous the 11th participant
-    reply = participant_stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-    server.stop(0)
-
-    assert reply.response == coordinator_pb2.RendezvousResponse.LATER
-
-
-@pytest.mark.integration
-def test_heartbeat(participant_stub, coordinator_service):
-    # first we need to rendezvous so that the participant is added to the list of participants
-    _ = participant_stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-    reply = participant_stub.Heartbeat(coordinator_pb2.HeartbeatRequest())
-
-    # the Coordinator is initialised in conftest.py::coordinator_service with 10 participants
-    # needed per round. so here we expect the HeartbeatReply to have State.STANDBY
-    # because we connected only one participant
-    assert reply == coordinator_pb2.HeartbeatReply()
-    assert coordinator_service.coordinator.state == coordinator_pb2.State.STANDBY
-
-
-@pytest.mark.integration
-def test_heartbeat_denied(participant_stub, coordinator_service):
-    # heartbeat requests are only allowed if the participant has already
-    # rendezvous with the coordinator
-    with pytest.raises(grpc.RpcError):
-        reply = participant_stub.Heartbeat(coordinator_pb2.HeartbeatRequest())
-        assert reply.status_code == grpc.StatusCode.PERMISSION_DENIED
-
-
-@mock.patch("threading.Event.is_set", side_effect=[False, True])
-@mock.patch("time.sleep", return_value=None)
-@mock.patch("xain_fl.coordinator.coordinator.Coordinator.remove_participant")
-def test_monitor_heartbeats(mock_participants_remove, _mock_sleep, _mock_event):
-    participants = Participants()
-    participants.add("participant_1")
-    participants.participants["participant_1"].heartbeat_expires = 0
-
-    coordinator = Coordinator()
-    coordinator.participants = participants
-
-    terminate_event = threading.Event()
-    monitor_heartbeats(coordinator, terminate_event)
-
-    mock_participants_remove.assert_called_once_with("participant_1")
-
-
-@mock.patch("threading.Event.is_set", side_effect=[False, True])
-@mock.patch("time.sleep", return_value=None)
-def test_monitor_heartbeats_remove_participant(_mock_sleep, _mock_event):
-    participants = Participants()
-    participants.add("participant_1")
-    participants.participants["participant_1"].heartbeat_expires = 0
-
-    coordinator = Coordinator()
-    coordinator.participants = participants
-
-    terminate_event = threading.Event()
-    monitor_heartbeats(coordinator, terminate_event)
-
-    assert participants.len() == 0
-
-
-@mock.patch("threading.Event.is_set", side_effect=[False, False, True])
-@mock.patch("time.sleep", return_value=None)
-@mock.patch("xain_fl.cproto.coordinator_pb2.HeartbeatRequest")
-def test_participant_heartbeat(mock_heartbeat_request, _mock_sleep, _mock_event):
-    channel = mock.MagicMock()
-    terminate_event = threading.Event()
-    st = StateRecord()
-
-    message_loop(channel, st, terminate_event)
-
-    # check that the heartbeat is sent exactly twice
-    mock_heartbeat_request.assert_has_calls([mock.call(), mock.call()])
-
-
-@pytest.mark.skip("Skipping due to moving of the grpc participant as sdk to xain-sdk")
-@pytest.mark.integration
-def test_start_training(coordinator_service):
-    test_weights = [np.arange(10), np.arange(10, 20)]
-
-    # set coordinator global model and hyper-params so that it needs only 1 participant
-    coord = coordinator_service.coordinator
-    coord.minimum_participants_in_round = 1
-    coord.fraction_of_participants = 1.0
-    coord.epochs = 5
-    coord.epoch_base = 2
-    coord.weights = test_weights
-    coord.minimum_connected_participants = coord.get_minimum_connected_participants()
-
-    # simulate a participant communicating with coordinator via channel
-    with grpc.insecure_channel("localhost:50051") as channel:
-        # we need to rendezvous before we can send any other requests
-        rendezvous(channel)
-        # call startTraining service method on coordinator
-        weights, epochs, epoch_base = start_training(channel)
-
-    # check global model received
-    assert epochs == 5
-    assert epoch_base == 2
-    np.testing.assert_equal(weights, test_weights)
-
-
-@pytest.mark.integration
-def test_start_training_denied(participant_stub, coordinator_service):
-    # start training requests are only allowed if the participant has already
-    # rendezvous with the coordinator
-    with pytest.raises(grpc.RpcError):
-        reply = participant_stub.StartTraining(coordinator_pb2.StartTrainingRequest())
-        assert reply.status_code == grpc.StatusCode.PERMISSION_DENIED
-
-
-@pytest.mark.skip("Skipping due to moving of the grpc participant as sdk to xain-sdk")
-@pytest.mark.integration
-def test_start_training_failed_precondition(participant_stub, coordinator_service):
-    # start training requests are only allowed if the coordinator is in ROUND state.
-    # Since we need 10 participants to be connected (see conftest.py::coordinator_service)
-    # the StartTrainingRequest is expected to fail
-    participant_stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-    with pytest.raises(grpc.RpcError):
-        reply = participant_stub.StartTraining(coordinator_pb2.StartTrainingRequest())
-        assert reply.status_code == grpc.StatusCode.FAILED_PRECONDITION
-
-
-@pytest.mark.skip("Skipping due to moving of the grpc participant as sdk to xain-sdk")
-@pytest.mark.integration
-def test_end_training(coordinator_service):
-    assert coordinator_service.coordinator.round.updates == {}
-
-    # simulate trained local model data
-    test_weights, number_samples = [np.arange(20, 30), np.arange(30, 40)], 2
-    metrics = {"metric": [np.arange(10, 20), np.arange(5, 10)]}
-
-    with grpc.insecure_channel("localhost:50051") as channel:
-        # we first need to rendezvous before we can send any other request
-        rendezvous(channel)
-        # call endTraining service method on coordinator
-        end_training(  # pylint: disable-msg=no-value-for-parameter
-            channel, test_weights, number_samples, metrics
-        )
-    # check local model received...
-
-    assert len(coordinator_service.coordinator.round.updates) == 1
-
-    round_ = coordinator_service.coordinator.round
-
-    # first the weights update
-    _, update = round_.updates.popitem()
-    tu1, tu2 = update["weight_update"]
-    assert tu2 == number_samples
-    np.testing.assert_equal(tu1, test_weights)
-
-    m = update["metrics"]
-    assert m.keys() == metrics.keys()
-    for k, vals in metrics.items():
-        np.testing.assert_allclose(m[k], vals)
-
-
-@pytest.mark.integration
-def test_end_training_duplicated_updates(coordinator_service, participant_stub):
-    # participant can only send updates once in a single round
-    participant_stub.Rendezvous(coordinator_pb2.RendezvousRequest())
-
-    participant_stub.EndTraining(coordinator_pb2.EndTrainingRequest())
-
-    with pytest.raises(grpc.RpcError):
-        reply = participant_stub.EndTraining(coordinator_pb2.EndTrainingRequest())
-        assert reply.status_code == grpc.StatusCode.ALREADY_EXISTS
-
-
-@pytest.mark.integration
-def test_end_training_denied(participant_stub, coordinator_service):
-    # heartbeat requests are only allowed if the participant has already
-    # rendezvous with the coordinator
-    with pytest.raises(grpc.RpcError):
-        reply = participant_stub.EndTraining(coordinator_pb2.EndTrainingRequest())
-        assert reply.status_code == grpc.StatusCode.PERMISSION_DENIED
diff --git a/xain_fl/cproto/test_participant.py b/xain_fl/cproto/test_participant.py
deleted file mode 100644
index 1d8dd168d..000000000
--- a/xain_fl/cproto/test_participant.py
+++ /dev/null
@@ -1,106 +0,0 @@
-from xain_fl.cproto import coordinator_pb2
-from xain_fl.cproto.participant import ParState, StateRecord, transit
-
-
-def test_from_start():
-    st = StateRecord()
-    assert st.lookup() == (ParState.WAITING_FOR_SELECTION, 0)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.ROUND)
-    transit(st, hb)
-    assert st.lookup() == (ParState.TRAINING, 0)
-    # should return immediately
-    assert st.wait_until_selected_or_done() == ParState.TRAINING
-
-
-def test_waiting_to_training_i():
-    st = StateRecord(state=ParState.WAITING_FOR_SELECTION)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.ROUND, round=1)
-    transit(st, hb)
-    assert st.lookup() == (ParState.TRAINING, 1)
-    # should return immediately
-    assert st.wait_until_selected_or_done() == ParState.TRAINING
-
-
-def test_waiting_to_done():
-    st = StateRecord(state=ParState.WAITING_FOR_SELECTION, round=2)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.FINISHED)
-    transit(st, hb)
-    assert st.lookup() == (ParState.DONE, 2)
-    # should return immediately
-    assert st.wait_until_selected_or_done() == ParState.DONE
-
-
-def test_waiting_to_waiting():
-    st = StateRecord(state=ParState.WAITING_FOR_SELECTION, round=3)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.STANDBY)
-    transit(st, hb)
-    assert st.lookup() == (ParState.WAITING_FOR_SELECTION, 3)
-
-
-def test_training_to_training():
-    st = StateRecord(state=ParState.TRAINING, round=4)
-    start_state = st.lookup()
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.STANDBY)
-    transit(st, hb)
-    assert st.lookup() == start_state
-    hb.state = coordinator_pb2.ROUND
-    transit(st, hb)
-    assert st.lookup() == start_state
-    hb.state = coordinator_pb2.FINISHED
-    transit(st, hb)
-    assert st.lookup() == start_state
-
-
-def test_posttraining_to_training():
-    st = StateRecord(state=ParState.POST_TRAINING, round=5)
-    start_state = st.lookup()
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.ROUND, round=5)
-    transit(st, hb)
-    assert st.lookup() == start_state
-    # old round? shouldn't affect me...
-    hb.round = 0
-    transit(st, hb)
-    assert st.lookup() == start_state
-    # NOTE a "future" round e.g. 7 would be unexpected under current assumptions
-    # it should be preceded by a STANDBY to indicate nonselection for round 6
-
-    # selected for next round
-    hb.round = 6
-    transit(st, hb)
-    assert st.lookup() == (ParState.TRAINING, 6)
-    # should return immediately
-    assert st.wait_until_next_round() == ParState.TRAINING
-
-
-def test_posttraining_to_done():
-    st = StateRecord(state=ParState.POST_TRAINING, round=6)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.FINISHED)
-    transit(st, hb)
-    assert st.lookup() == (ParState.DONE, 6)
-    # should return immediately
-    assert st.wait_until_next_round() == ParState.DONE
-
-
-def test_posttraining_to_waiting():
-    st = StateRecord(state=ParState.POST_TRAINING, round=7)
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.STANDBY)
-    transit(st, hb)
-    assert st.lookup() == (ParState.WAITING_FOR_SELECTION, 7)
-    # should return immediately
-    assert st.wait_until_next_round() == ParState.WAITING_FOR_SELECTION
-
-
-def test_restart_round():
-    # participant has done its training for round 8
-    st = StateRecord(state=ParState.POST_TRAINING, round=8)
-    # it's told to go into waiting
-    hb = coordinator_pb2.HeartbeatReply(state=coordinator_pb2.STANDBY)
-    transit(st, hb)
-    assert st.lookup() == (ParState.WAITING_FOR_SELECTION, 8)
-    # and back again to training...
-    hb.state = coordinator_pb2.ROUND
-    hb.round = 8  # but still in round 8!
-    # => interpret this as "round restarted" e.g. original theta was corrupt or something
-    transit(st, hb)
-    # => re-do the training...
-    assert st.lookup() == (ParState.TRAINING, 8)
diff --git a/xain_fl/datasets/__init__.py b/xain_fl/datasets/__init__.py
deleted file mode 100644
index 6f86323de..000000000
--- a/xain_fl/datasets/__init__.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-
-from absl import flags
-
-import xain_fl.config
-
-from .dataset import load_splits
-
-c = xain_fl.config.load()
-
-FLAGS = flags.FLAGS
-
-flags.DEFINE_string(
-    "local_datasets_dir",
-    c.get("Path", "local_datasets_dir"),
-    "Local directory to store datasets in. Usually ~/.xain-fl/datasets",
-)
-flags.DEFINE_string(
-    "datasets_repository",
-    "http://xain-datasets.s3-website.eu-central-1.amazonaws.com",
-    "Remote datasets repository.",
-)
-flags.DEFINE_boolean(
-    "fetch_datasets",
-    True,
-    "Indicates if remote datasets should be fetched if required.",
-)
diff --git a/xain_fl/datasets/conftest.py b/xain_fl/datasets/conftest.py
deleted file mode 100644
index 30e4a5926..000000000
--- a/xain_fl/datasets/conftest.py
+++ /dev/null
@@ -1,59 +0,0 @@
-import numpy as np
-import pytest
-
-
-@pytest.fixture
-def mock_simple_keras_dataset():
-    # train set with labels 0, 7, 1, 2 as 3x3 images/matrixes
-    x_train = np.array(
-        [
-            [[1, 1, 1], [1, 0, 1], [1, 1, 1]],
-            [[1, 1, 1], [0, 1, 0], [1, 0, 0]],
-            [[0, 1, 0], [0, 1, 0], [0, 1, 0]],
-            [[1, 1, 1], [0, 1, 0], [1, 1, 1]],
-        ],
-        dtype=np.int8,
-    )
-    y_train = np.array([0, 7, 1, 2], dtype=np.int8)
-
-    # test set with label 1 and corresponding matrix (x_train[0])
-    x_test = np.array([[[0, 0, 1], [0, 0, 1], [0, 0, 1]]], dtype=np.int8)
-    y_test = np.array([1], dtype=np.int8)
-
-    assert x_train.shape[0] == y_train.shape[0]
-    assert x_test.shape[0] == y_test.shape[0]
-
-    assert x_train.shape[1] == x_test.shape[1]
-    assert x_train.shape[2] == x_test.shape[2]
-
-    return (x_train, y_train), (x_test, y_test)
-
-
-@pytest.fixture
-def mock_simple_federated_dataset():
-    # train set with numbers 0, 7, 1, 2 as 3x3 images/matrixes
-    x_train = np.array(
-        [
-            [[1, 1, 1], [1, 0, 1], [1, 1, 1]],
-            [[0, 1, 0], [0, 1, 0], [0, 1, 0]],
-            [[1, 1, 1], [0, 1, 0], [1, 0, 0]],
-        ],
-        dtype=np.int8,
-    )
-    y_train = np.array([0, 1, 7], dtype=np.int8)
-
-    x_val = np.array([[[1, 1, 1], [0, 1, 0], [1, 1, 1]]], dtype=np.int8)
-    y_val = np.array([2], dtype=np.int8)
-
-    # test set with number 1 as matrix
-    x_test = np.array([[[0, 0, 1], [0, 0, 1], [0, 0, 1]]], dtype=np.int8)
-    y_test = np.array([1], dtype=np.int8)
-
-    assert x_train.shape[0] == y_train.shape[0]
-    assert x_val.shape[0] == y_val.shape[0]
-    assert x_test.shape[0] == y_test.shape[0]
-
-    x_splits = np.split(x_train, indices_or_sections=3, axis=0)
-    y_splits = np.split(y_train, indices_or_sections=3, axis=0)
-
-    return zip(x_splits, y_splits), (x_val, y_val), (x_test, y_test)
diff --git a/xain_fl/datasets/dataset.py b/xain_fl/datasets/dataset.py
deleted file mode 100644
index 66325144c..000000000
--- a/xain_fl/datasets/dataset.py
+++ /dev/null
@@ -1,73 +0,0 @@
-import tensorflow as tf
-from absl import flags
-
-from xain_fl.datasets import storage
-from xain_fl.types import FederatedDataset
-
-FLAGS = flags.FLAGS
-
-cifar10 = tf.keras.datasets.cifar10
-fashion_mnist = tf.keras.datasets.fashion_mnist
-
-config = {
-    # cpp datasets
-    "cifar-10-100p-noniid-01cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-02cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-03cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-04cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-05cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-06cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-07cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-08cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-noniid-09cpp": {"keras_dataset": cifar10},
-    "cifar-10-100p-iid-balanced": {"keras_dataset": cifar10},
-    "fashion-mnist-100p-noniid-01cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-02cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-03cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-04cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-05cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-06cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-07cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-08cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-noniid-09cpp": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-iid-balanced": {"keras_dataset": fashion_mnist},
-    # volume based datasets
-    "cifar-10-100p-b1_000": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_005": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_010": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_015": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_020": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_025": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_030": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_035": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_040": {"keras_dataset": cifar10},
-    "cifar-10-100p-b1_045": {"keras_dataset": cifar10},
-    "fashion-mnist-100p-b1_000": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_005": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_010": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_015": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_020": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_025": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_030": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_035": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_040": {"keras_dataset": fashion_mnist},
-    "fashion-mnist-100p-b1_045": {"keras_dataset": fashion_mnist},
-}
-
-
-def load_splits(
-    dataset_name: str, get_local_datasets_dir=storage.default_get_local_datasets_dir
-) -> FederatedDataset:
-    """Will load and return federated dataset
-
-        Args:
-            dataset_name (str): Name of dataset to be loaded. Valid names can be found
-                                in xain_fl.datasets.dataset.config dict
-            get_local_datasets_dir (Callable): Function which returns the local_datasets_dir
-
-        Returns:
-            FederatedDataset
-    """
-    return storage.load_splits(
-        dataset_name=dataset_name, get_local_datasets_dir=get_local_datasets_dir
-    )
diff --git a/xain_fl/datasets/dataset_test.py b/xain_fl/datasets/dataset_test.py
deleted file mode 100644
index 5535b5f48..000000000
--- a/xain_fl/datasets/dataset_test.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import random
-
-import numpy as np
-import pytest
-
-from .dataset import config, load_splits
-
-
-@pytest.mark.slow
-@pytest.mark.integration
-def test_load_splits(tmp_path):
-    # Prepare
-    def get_local_datasets_dir():
-        return tmp_path
-
-    dataset_name = random.choice(list(config))
-
-    # Execute
-    xy_splits_actual, xy_val_actual, xy_test_actual = load_splits(
-        dataset_name=dataset_name, get_local_datasets_dir=get_local_datasets_dir
-    )
-
-    # Assert
-    assert isinstance(xy_splits_actual, list)
-    assert isinstance(xy_val_actual, tuple)
-    assert isinstance(xy_test_actual, tuple)
-
-    for xy in xy_splits_actual:
-        x, y = xy
-
-        assert isinstance(x, np.ndarray)
-        assert isinstance(y, np.ndarray)
-
-
-@pytest.mark.integration
-def test_load_splits_without_fetch(tmp_path, disable_fetch):  # pylint: disable=W0613
-    # Prepare
-    dataset_name = list(config)[0]
-
-    def get_local_datasets_dir():
-        return tmp_path
-
-    # Execute
-    with pytest.raises(Exception):
-        load_splits(
-            dataset_name=dataset_name, get_local_datasets_dir=get_local_datasets_dir
-        )
diff --git a/xain_fl/datasets/hashes/README.md b/xain_fl/datasets/hashes/README.md
deleted file mode 100644
index 2dec44320..000000000
--- a/xain_fl/datasets/hashes/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# Dataset Hashes
-
-This directory contains lists of hashes for all online datasets for validation
diff --git a/xain_fl/datasets/hashes/__init__.py b/xain_fl/datasets/hashes/__init__.py
deleted file mode 100644
index 9a77c3f65..000000000
--- a/xain_fl/datasets/hashes/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-from . import load
-
-datasets = load.load_hashes()
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_000.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_000.json
deleted file mode 100644
index 690b86896..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_000.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
-  "00": [
-    "3c8bd2aaba35e4b30181c7262de4e0581b9596f3",
-    "4bf9373934382dca2d410cf6691a984114b3621c"
-  ],
-  "01": [
-    "4e67e76b101243f25cf29ae81f8846886d2cb130",
-    "21177918a4bc57bc19f3ac88b9021805b252a6ef"
-  ],
-  "02": [
-    "fe2959a5f965b9fcfc5e7bfd2743fdae8f7e0e81",
-    "0668ee2c20efc824ddcd4418db496a8556f51634"
-  ],
-  "03": [
-    "c57aaedef43a9ef7afba28cfc4db65708fc02439",
-    "a574769abe833b49065ab7f6637162b07f866ff7"
-  ],
-  "04": [
-    "41a2ca080e339c7b8f33275b53bb36509a10a38b",
-    "8bcc8e85cb0b6d56112e2de7f169760ae9b165de"
-  ],
-  "05": [
-    "d5974afde2857f5810677da884eaf229f6db63a2",
-    "fdbf4fc01ccdac07825c2cac95ff7cb826091178"
-  ],
-  "06": [
-    "68a6fe55d8053b604d6f7e94823ef191739d9efb",
-    "fc1393c5bf6c3edddec4bb3fcb63d05627e57f65"
-  ],
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_005.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_005.json
deleted file mode 100644
index 124e357cf..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_005.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_010.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_010.json
deleted file mode 100644
index 8dfd1f0df..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_010.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_015.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_015.json
deleted file mode 100644
index 407ade6c9..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_015.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_020.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_020.json
deleted file mode 100644
index ba825ed14..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_020.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_025.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_025.json
deleted file mode 100644
index d618af346..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_025.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_030.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_030.json
deleted file mode 100644
index bd510ad4c..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_030.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_035.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_035.json
deleted file mode 100644
index bce974d14..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_035.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_040.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_040.json
deleted file mode 100644
index 3181c3ede..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_040.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-b1_045.json b/xain_fl/datasets/hashes/cifar-10-100p-b1_045.json
deleted file mode 100644
index 831136e43..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-b1_045.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-iid-balanced.json b/xain_fl/datasets/hashes/cifar-10-100p-iid-balanced.json
deleted file mode 100644
index b716583c6..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-iid-balanced.json
+++ /dev/null
@@ -1,410 +0,0 @@
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-01cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-01cpp.json
deleted file mode 100644
index d96989784..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-01cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-02cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-02cpp.json
deleted file mode 100644
index 6bee7940d..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-02cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-  "test": [
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-  "val": [
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-    "faaa5f5d3329dc4e961fc0f7dfaa3c1266fccb2f"
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-03cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-03cpp.json
deleted file mode 100644
index 86f41842b..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-03cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-04cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-04cpp.json
deleted file mode 100644
index f71ebf567..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-04cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-05cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-05cpp.json
deleted file mode 100644
index 5e8279228..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-05cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-06cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-06cpp.json
deleted file mode 100644
index 404e183e0..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-06cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-  ],
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-07cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-07cpp.json
deleted file mode 100644
index a751bc953..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-07cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-08cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-08cpp.json
deleted file mode 100644
index 3656b351f..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-08cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/cifar-10-100p-noniid-09cpp.json b/xain_fl/datasets/hashes/cifar-10-100p-noniid-09cpp.json
deleted file mode 100644
index c1511bcf7..000000000
--- a/xain_fl/datasets/hashes/cifar-10-100p-noniid-09cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_000.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_000.json
deleted file mode 100644
index 4ca7b17c9..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_000.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_005.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_005.json
deleted file mode 100644
index 9640850c2..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_005.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_010.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_010.json
deleted file mode 100644
index bce605918..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_010.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_015.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_015.json
deleted file mode 100644
index b578faacd..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_015.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_020.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_020.json
deleted file mode 100644
index bd84ee39a..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_020.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_025.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_025.json
deleted file mode 100644
index 4a10d93e4..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_025.json
+++ /dev/null
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_030.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_030.json
deleted file mode 100644
index 4d222cc96..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_030.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_035.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_035.json
deleted file mode 100644
index 63d02cf7e..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_035.json
+++ /dev/null
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_040.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_040.json
deleted file mode 100644
index 181f138fe..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_040.json
+++ /dev/null
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_045.json b/xain_fl/datasets/hashes/fashion-mnist-100p-b1_045.json
deleted file mode 100644
index 22e478c45..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-b1_045.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-iid-balanced.json b/xain_fl/datasets/hashes/fashion-mnist-100p-iid-balanced.json
deleted file mode 100644
index 4af4ffef4..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-iid-balanced.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-01cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-01cpp.json
deleted file mode 100644
index 9f5cb04ec..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-01cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-02cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-02cpp.json
deleted file mode 100644
index 8e2520bde..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-02cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-03cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-03cpp.json
deleted file mode 100644
index c8fbf2965..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-03cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-04cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-04cpp.json
deleted file mode 100644
index 2f8e94ba9..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-04cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-05cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-05cpp.json
deleted file mode 100644
index b7c36e148..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-05cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-06cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-06cpp.json
deleted file mode 100644
index d6f130d6c..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-06cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
-{
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-07cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-07cpp.json
deleted file mode 100644
index 24c3f6a78..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-07cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-08cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-08cpp.json
deleted file mode 100644
index 95a7ea043..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-08cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
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\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-09cpp.json b/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-09cpp.json
deleted file mode 100644
index 03e215451..000000000
--- a/xain_fl/datasets/hashes/fashion-mnist-100p-noniid-09cpp.json
+++ /dev/null
@@ -1,410 +0,0 @@
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-    "37437d8270c737f355adbd3a5328f190f7525ec6"
-  ],
-  "66": [
-    "e5cfd3fd42b5a4d6e216b7b6e8ea4a542c7f4abd",
-    "899f4ef767ed15398328604c3e1fc29680947007"
-  ],
-  "67": [
-    "eb31d7e2931a5df08be9eebe7adfa5bb53393c27",
-    "ea3667d4ce4777fc80c52f59214f6a0f0fb07074"
-  ],
-  "68": [
-    "8867cda582a94381a3c4f24b417efd59138da4ce",
-    "5a03738e7634b1e02dba87fa0d0ec862c95f4ea6"
-  ],
-  "69": [
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-  ],
-  "70": [
-    "708683fb8b5a856b118b52dc48ed4e6f6b6feb2a",
-    "60eec0e26df7d5c9d13b40fd43180ba8d94ded11"
-  ],
-  "71": [
-    "1b88ac329fb1cd64a0a2dd4c03a0c74042b0d110",
-    "2b8417ceaf08cc7e195de5ab7902cedbaef1a246"
-  ],
-  "72": [
-    "4921db7f7c8a1f5bc6d4df85e4c4d37789b04845",
-    "10cf1fbe4065a470343b1efbccc402e4801fe834"
-  ],
-  "73": [
-    "b6d06859dfedcbb4d7ca6a209876ea49c08453ce",
-    "ea3667d4ce4777fc80c52f59214f6a0f0fb07074"
-  ],
-  "74": [
-    "90d2d38a1ea6d7b16004e237aa0fb9381e2b5ce5",
-    "4adfed9783185914e573f4c0f059a9adc6ab8cf0"
-  ],
-  "75": [
-    "38bd96637543cf774909c4012fed4e894a04dc1b",
-    "b5a6ba17113e273091faf377ad53aabb78f6f845"
-  ],
-  "76": [
-    "7c128e8a7855f5613ac02bd61f92b490b685b8b0",
-    "899f4ef767ed15398328604c3e1fc29680947007"
-  ],
-  "77": [
-    "c3ad3ec14f53de87f0592302e67b9e673dd2f3b9",
-    "ea3667d4ce4777fc80c52f59214f6a0f0fb07074"
-  ],
-  "78": [
-    "f971a9f7364215c4dd1d81755f95fe0c917d3005",
-    "37437d8270c737f355adbd3a5328f190f7525ec6"
-  ],
-  "79": [
-    "a2a0c32e855dcfca069b5218ea0dbe29c4ea1728",
-    "899f4ef767ed15398328604c3e1fc29680947007"
-  ],
-  "80": [
-    "4b43ae58b7e868cb4bc7a39b4fb569ab461a9be7",
-    "899f4ef767ed15398328604c3e1fc29680947007"
-  ],
-  "81": [
-    "86f75fa3387db6b50b6342be8759189d0d940ca5",
-    "b5a6ba17113e273091faf377ad53aabb78f6f845"
-  ],
-  "82": [
-    "feaff0f6c3a1f255f30237deaccd1ad094b54862",
-    "5a03738e7634b1e02dba87fa0d0ec862c95f4ea6"
-  ],
-  "83": [
-    "bba3ced7c7ff5e143b58198cf55e42c66afa50b0",
-    "1e667f8aff944436242fcfcb842275117ccd0fd5"
-  ],
-  "84": [
-    "f58e61bdc3e6e15ebaf5b8c4e8830fc5358482f9",
-    "60eec0e26df7d5c9d13b40fd43180ba8d94ded11"
-  ],
-  "85": [
-    "c3b866c33e06387958140eb9def5ade1f15fc546",
-    "b5a6ba17113e273091faf377ad53aabb78f6f845"
-  ],
-  "86": [
-    "cd777107e7d9848bfbaa6c728607b212e27ce045",
-    "2b8417ceaf08cc7e195de5ab7902cedbaef1a246"
-  ],
-  "87": [
-    "7ac237da1102c9b1d733a6dc517e60c69157cbdb",
-    "37437d8270c737f355adbd3a5328f190f7525ec6"
-  ],
-  "88": [
-    "eaca5dadcbdac2422d417c6ab35b68dc678a3f0a",
-    "2b8417ceaf08cc7e195de5ab7902cedbaef1a246"
-  ],
-  "89": [
-    "53af32f87c277fec2ef1c2efe1dd0d229761b746",
-    "2b8417ceaf08cc7e195de5ab7902cedbaef1a246"
-  ],
-  "90": [
-    "ad62c7f17b317d6c4c95d9ad9db721cd15a66827",
-    "2b8417ceaf08cc7e195de5ab7902cedbaef1a246"
-  ],
-  "91": [
-    "064cf385927d7571606873f2daa36dc02316e102",
-    "37437d8270c737f355adbd3a5328f190f7525ec6"
-  ],
-  "92": [
-    "a516a167ecba40568c138b7da1c40d79b1c9c469",
-    "b5a6ba17113e273091faf377ad53aabb78f6f845"
-  ],
-  "93": [
-    "e718c439d35a7e61f195558a673d22346d0f12cd",
-    "899f4ef767ed15398328604c3e1fc29680947007"
-  ],
-  "94": [
-    "ca68f6b9bc7f2a1d964c520ab9601d9cbef35248",
-    "ea3667d4ce4777fc80c52f59214f6a0f0fb07074"
-  ],
-  "95": [
-    "8ebe3400240d32c4491d3cb7480a99062553caec",
-    "1e667f8aff944436242fcfcb842275117ccd0fd5"
-  ],
-  "96": [
-    "7bbd461b5bc0c043bd6bf30987610fd8a543734a",
-    "1e667f8aff944436242fcfcb842275117ccd0fd5"
-  ],
-  "97": [
-    "2553bbdbd619be152e369f0eabe9c4c56addc313",
-    "60eec0e26df7d5c9d13b40fd43180ba8d94ded11"
-  ],
-  "98": [
-    "6991068061aeac489dd1e1e55ca78e7c9b59b498",
-    "60eec0e26df7d5c9d13b40fd43180ba8d94ded11"
-  ],
-  "99": [
-    "72037cf8a6f20a982f8d6aa20a16e1038706f2c6",
-    "10cf1fbe4065a470343b1efbccc402e4801fe834"
-  ],
-  "test": [
-    "79e6584f3574e22e97dfe17ddf1b9856b3f2284f",
-    "b056ffe622e9a6cfb76862c5ecb120c73d4fd99e"
-  ],
-  "val": [
-    "c008c5dcaf03d23962756f92e6cd09a902f20f8b",
-    "e67663f832358b645232f0d448d84e4d6e8c65cd"
-  ]
-}
\ No newline at end of file
diff --git a/xain_fl/datasets/hashes/load.py b/xain_fl/datasets/hashes/load.py
deleted file mode 100644
index 8ad462cfb..000000000
--- a/xain_fl/datasets/hashes/load.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import glob
-import json
-import os
-
-
-def load_hashes():
-    hashes = {}
-    path = os.path.dirname(__file__)
-
-    json_pattern = os.path.join(path, "*.json")
-    file_list = glob.glob(json_pattern)
-
-    for fname in file_list:
-        with open(fname) as f:
-            dict_key = os.path.basename(fname)[0:-5]
-            hashes[dict_key] = json.loads(f.read())
-
-    return hashes
diff --git a/xain_fl/datasets/hashes/load_test.py b/xain_fl/datasets/hashes/load_test.py
deleted file mode 100644
index 483bc13a0..000000000
--- a/xain_fl/datasets/hashes/load_test.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from xain_fl.datasets.hashes import load
-
-
-def test_load_hashes():
-    # Execute
-    dataset_hashes = load.load_hashes()
-
-    # Prepare
-    assert isinstance(dataset_hashes, dict)
-    assert "fashion-mnist-100p-noniid-01cpp" in dataset_hashes.keys()
-    assert len(dataset_hashes["fashion-mnist-100p-noniid-01cpp"].keys()) == 102
diff --git a/xain_fl/datasets/prep.py b/xain_fl/datasets/prep.py
deleted file mode 100644
index 05b599eb4..000000000
--- a/xain_fl/datasets/prep.py
+++ /dev/null
@@ -1,180 +0,0 @@
-import numpy as np
-import tensorflow as tf
-from tensorflow.data import Dataset
-
-from xain_fl.types import Partition
-
-AUTOTUNE = tf.data.experimental.AUTOTUNE
-SEED = 2017
-
-
-def init_ds_train(
-    xy: Partition, num_classes=10, batch_size=32, augmentation=False
-) -> Dataset:
-    """Initializes federated train dataset partition. Will return a TensorFlow dataset.
-
-    Args:
-        xy (Partition): Tuple of two ndarrays corresponding to (examples, classes)
-        num_classes (int): Number of classes present in parition
-        batch_size (int): Number of examples in each batch
-        augmentation (bool): Enables automated augmentation of dataset e.g. random
-            cropping or random hue, saturation, brightness or contrast adjustment
-
-    Returns:
-        Dataset: TensorFlow dataset
-    """
-    return _init_ds(xy, num_classes, batch_size, augmentation, shuffle=True)
-
-
-def init_ds_val(xy: Partition, num_classes=10) -> Dataset:
-    """Initializes validation partition. Will return a TensorFlow dataset.
-
-    Args:
-        xy (Partition): Tuple of two ndarrays corresponding to (examples, classes)
-        num_classes (int): Number of classes present in federated dataset partition
-
-    Returns:
-        Dataset: TensorFlow dataset
-    """
-    batch_size = xy[0].shape[0]  # Return full dataset as one large batch
-    return _init_ds(xy, num_classes, batch_size, augmentation=False, shuffle=False)
-
-
-# pylint: disable-msg=too-many-arguments
-def _init_ds(
-    xy: Partition, num_classes: int, batch_size: int, augmentation: bool, shuffle: bool
-) -> Dataset:
-    (x, y) = xy
-    # Assume that each row in `x` corresponds to the same row in `y`
-    assert x.shape[0] == y.shape[0]
-    assert x.ndim == 3 or x.ndim == 4  # (Fashion-)MNIST: 3, CIFAR-10: 4
-    assert y.ndim == 1
-    # Add one dimension to grayscale-image datasets
-    grayscale = False
-    if x.ndim == 3:
-        grayscale = True
-        x = np.reshape(x, (x.shape[0], x.shape[1], x.shape[2], 1))
-    # Create tf.data.Dataset from ndarrays
-    ds = to_dataset(x, y)
-    # Data preparation:
-    # - Cast color channel values to float, divide by 255
-    # - One-hot encode labels
-    ds = prepare(ds, num_classes=num_classes)
-    # Data augmentation:
-    # - Randomize hue/saturation/brightness/contrast (CIFAR-10/non-grayscale only)
-    # - Take random 32x32 (or 28x28) crop (after padding to 40x40 (or 32x32))
-    # - Random horizontal flip
-    if augmentation:
-        ds = _augment_ds(ds, grayscale)
-    return batch_and_repeat(ds, batch_size, shuffle=shuffle, repeat=True)
-
-
-def to_dataset(x: np.ndarray, y: np.ndarray) -> Dataset:
-    """Creates a TensorFlow Dataset from two ndarrays
-
-    Args:
-        x (np.ndarray)
-        y (np.ndarray)
-
-    Returns:
-        Dataset
-    """
-    return Dataset.from_tensor_slices((x, y))
-
-
-def prepare(ds: Dataset, num_classes: int) -> Dataset:
-    """Prepares dataset for training by
-    - Casting color channel values to float, divide by 255
-    - One-hot encode labels
-
-    Args:
-        ds (Dataset): TensorFlow Dataset
-        num_classes (int): Number of classes present in federated dataset partition
-
-    Returns:
-        Dataset
-    """
-    ds = ds.map(lambda x, y: (x, _prep_cast_label(y)))
-    ds = ds.map(lambda x, y: (_prep_cast_divide(x), y))
-    ds = ds.map(lambda x, y: (x, _prep_one_hot(y, num_classes)))
-    return ds
-
-
-def _augment_ds(ds: Dataset, grayscale: bool) -> Dataset:
-    if not grayscale:
-        ds = ds.map(
-            lambda x, y: (_random_hue_saturation_brightness_contrast(x), y),
-            num_parallel_calls=AUTOTUNE,
-        )
-    if grayscale:
-        ds = ds.map(
-            lambda x, y: (_random_crop_mnist(x), y), num_parallel_calls=AUTOTUNE
-        )
-    else:
-        ds = ds.map(
-            lambda x, y: (_random_crop_cifar(x), y), num_parallel_calls=AUTOTUNE
-        )
-    ds = ds.map(
-        lambda x, y: (_random_horizontal_flip(x), y), num_parallel_calls=AUTOTUNE
-    )
-    return ds
-
-
-def batch_and_repeat(
-    ds: Dataset, batch_size: int, shuffle: bool, repeat: bool
-) -> Dataset:
-    """Helper method for to apply tensorflow shuffle, repeat and
-    batch (in this order)
-
-    Args:
-        ds (Dataset): Tensorflow Dataset
-        batch_size (int): Will call ds.batch(batch_size, drop_remainder=False)
-            if batch_size is greater zero
-        shuffle (int): Will call ds.shuffle(1024)
-        repeat (bool): Will call ds.repeat()
-
-    Returns:
-        Dataset
-    """
-    ds = ds.prefetch(buffer_size=AUTOTUNE)
-    if shuffle:
-        ds = ds.shuffle(1024, seed=SEED)
-    if repeat:
-        ds = ds.repeat()
-    if batch_size > 0:
-        ds = ds.batch(batch_size, drop_remainder=False)
-    return ds
-
-
-def _prep_cast_label(y: tf.Tensor) -> tf.Tensor:
-    return tf.cast(y, tf.int64)
-
-
-def _prep_cast_divide(x: tf.Tensor) -> tf.Tensor:
-    return tf.cast(x, tf.float32) / 255
-
-
-def _prep_one_hot(y: tf.Tensor, num_classes: int) -> tf.Tensor:
-    return tf.one_hot(y, num_classes)
-
-
-def _random_crop_mnist(img: tf.Tensor) -> tf.Tensor:
-    img_padded = tf.image.pad_to_bounding_box(img, 2, 2, 32, 32)
-    return tf.image.random_crop(img_padded, size=[28, 28, 1], seed=SEED)
-
-
-def _random_crop_cifar(img: tf.Tensor) -> tf.Tensor:
-    img_padded = tf.image.pad_to_bounding_box(img, 4, 4, 40, 40)
-    return tf.image.random_crop(img_padded, size=[32, 32, 3], seed=SEED)
-
-
-def _random_horizontal_flip(img: tf.Tensor) -> tf.Tensor:
-    return tf.image.random_flip_left_right(img, seed=SEED)
-
-
-def _random_hue_saturation_brightness_contrast(img: tf.Tensor) -> tf.Tensor:
-    img = tf.image.random_hue(img, 0.08, seed=SEED)
-    img = tf.image.random_saturation(img, 0.6, 1.6, seed=SEED)
-    img = tf.image.random_brightness(img, 0.05, seed=SEED)
-    img = tf.image.random_contrast(img, 0.7, 1.3, seed=SEED)
-    return img
diff --git a/xain_fl/datasets/prep_test.py b/xain_fl/datasets/prep_test.py
deleted file mode 100644
index ce284dcca..000000000
--- a/xain_fl/datasets/prep_test.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import tensorflow as tf
-
-from . import prep
-
-
-def test_init_dataset(mock_keras_dataset):
-    # Prepare
-    xy, _ = mock_keras_dataset
-    # Execute
-    ds = prep.init_ds_train(xy)
-    # Assert
-    assert ds.output_types == (tf.float32, tf.float32)
-    assert len(ds.output_shapes) == 2
-    shape_x = ds.output_shapes[0]
-    assert len(shape_x) == 4
-    shape_y = ds.output_shapes[1]
-    assert len(shape_y) == 2
diff --git a/xain_fl/datasets/stats/__init__.py b/xain_fl/datasets/stats/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/xain_fl/datasets/stats/__main__.py b/xain_fl/datasets/stats/__main__.py
deleted file mode 100644
index dada552b4..000000000
--- a/xain_fl/datasets/stats/__main__.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import os
-
-from absl import app
-
-from xain_fl.datasets.dataset import config, load_splits
-
-from .stats import DSStats
-
-module_dir = os.path.dirname(__file__)  # directory in which this module resides
-stats_dir = os.path.abspath(os.path.join(module_dir, "datasets"))  # project root dir
-
-
-def main(_):
-    for dataset_name in config:
-        fname = os.path.join(stats_dir, f"{dataset_name}.txt")
-        with open(fname, "w") as f:
-            s = DSStats(name=dataset_name, ds=load_splits(dataset_name)).__repr__()
-
-            # Don't log with xain_fl.logger as repl in DSStats expects to
-            # be printed with print
-            print(s)
-
-            f.write(s)
-
-
-app.run(main=main)
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_000.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_000.txt
deleted file mode 100644
index 9552a872b..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_000.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_000
-number_of_examples_per_label_per_shard
-partition: 0000	total: 450	per_label:   48	  42	  35	  52	  47	  55	  43	  42	  38	  48
-partition: 0001	total: 450	per_label:   42	  43	  48	  47	  55	  41	  40	  41	  44	  49
-partition: 0002	total: 450	per_label:   55	  50	  43	  45	  43	  43	  42	  43	  41	  45
-partition: 0003	total: 450	per_label:   46	  53	  49	  42	  38	  35	  45	  50	  42	  50
-partition: 0004	total: 450	per_label:   33	  52	  53	  57	  41	  40	  35	  44	  57	  38
-partition: 0005	total: 450	per_label:   45	  48	  57	  38	  47	  42	  39	  40	  56	  38
-partition: 0006	total: 450	per_label:   55	  36	  56	  47	  39	  45	  48	  47	  35	  42
-partition: 0007	total: 450	per_label:   48	  31	  54	  38	  44	  52	  50	  45	  41	  47
-partition: 0008	total: 450	per_label:   49	  45	  51	  45	  34	  50	  50	  45	  29	  52
-partition: 0009	total: 450	per_label:   49	  38	  42	  55	  36	  40	  39	  58	  42	  51
-partition: 0010	total: 450	per_label:   35	  54	  44	  41	  48	  41	  40	  44	  59	  44
-partition: 0011	total: 450	per_label:   49	  40	  39	  41	  42	  40	  37	  59	  52	  51
-partition: 0012	total: 450	per_label:   34	  38	  51	  45	  39	  45	  39	  55	  48	  56
-partition: 0013	total: 450	per_label:   49	  48	  33	  37	  44	  42	  60	  47	  54	  36
-partition: 0014	total: 450	per_label:   45	  41	  46	  41	  55	  55	  53	  27	  46	  41
-partition: 0015	total: 450	per_label:   66	  40	  36	  40	  52	  48	  37	  40	  42	  49
-partition: 0016	total: 450	per_label:   46	  38	  44	  47	  39	  43	  53	  37	  48	  55
-partition: 0017	total: 450	per_label:   47	  31	  53	  48	  39	  56	  53	  41	  44	  38
-partition: 0018	total: 450	per_label:   51	  49	  53	  55	  56	  34	  42	  26	  39	  45
-partition: 0019	total: 450	per_label:   42	  49	  44	  39	  56	  46	  39	  40	  43	  52
-partition: 0020	total: 450	per_label:   37	  41	  44	  45	  54	  33	  52	  51	  44	  49
-partition: 0021	total: 450	per_label:   54	  47	  30	  46	  44	  41	  57	  40	  47	  44
-partition: 0022	total: 450	per_label:   49	  54	  46	  42	  53	  48	  43	  32	  33	  50
-partition: 0023	total: 450	per_label:   43	  45	  52	  34	  46	  43	  50	  37	  52	  48
-partition: 0024	total: 450	per_label:   57	  41	  43	  34	  46	  55	  42	  43	  40	  49
-partition: 0025	total: 450	per_label:   46	  53	  47	  40	  45	  43	  49	  45	  40	  42
-partition: 0026	total: 450	per_label:   43	  43	  56	  46	  46	  44	  45	  38	  39	  50
-partition: 0027	total: 450	per_label:   34	  47	  48	  46	  44	  41	  49	  58	  44	  39
-partition: 0028	total: 450	per_label:   45	  48	  47	  31	  40	  42	  50	  49	  51	  47
-partition: 0029	total: 450	per_label:   44	  51	  46	  41	  50	  35	  55	  43	  44	  41
-partition: 0030	total: 450	per_label:   56	  45	  49	  47	  45	  48	  34	  36	  42	  48
-partition: 0031	total: 450	per_label:   49	  46	  47	  46	  45	  52	  47	  34	  40	  44
-partition: 0032	total: 450	per_label:   52	  28	  39	  46	  52	  43	  55	  42	  50	  43
-partition: 0033	total: 450	per_label:   46	  43	  35	  64	  40	  41	  52	  41	  40	  48
-partition: 0034	total: 450	per_label:   47	  46	  51	  41	  43	  57	  37	  40	  49	  39
-partition: 0035	total: 450	per_label:   40	  61	  45	  44	  40	  40	  31	  42	  47	  60
-partition: 0036	total: 450	per_label:   45	  66	  30	  52	  34	  45	  45	  49	  42	  42
-partition: 0037	total: 450	per_label:   44	  37	  37	  42	  50	  56	  49	  45	  41	  49
-partition: 0038	total: 450	per_label:   42	  59	  38	  47	  56	  44	  42	  48	  39	  35
-partition: 0039	total: 450	per_label:   50	  48	  32	  52	  43	  48	  50	  48	  44	  35
-partition: 0040	total: 450	per_label:   35	  48	  39	  49	  50	  45	  52	  50	  43	  39
-partition: 0041	total: 450	per_label:   39	  49	  46	  41	  40	  48	  37	  50	  48	  52
-partition: 0042	total: 450	per_label:   48	  54	  33	  40	  44	  46	  47	  43	  57	  38
-partition: 0043	total: 450	per_label:   36	  47	  42	  44	  44	  46	  42	  40	  59	  50
-partition: 0044	total: 450	per_label:   45	  47	  54	  41	  54	  40	  47	  34	  47	  41
-partition: 0045	total: 450	per_label:   51	  50	  48	  47	  41	  48	  35	  43	  40	  47
-partition: 0046	total: 450	per_label:   43	  39	  47	  44	  45	  40	  58	  44	  43	  47
-partition: 0047	total: 450	per_label:   51	  45	  44	  49	  36	  46	  53	  45	  41	  40
-partition: 0048	total: 450	per_label:   29	  40	  41	  57	  50	  47	  47	  52	  34	  53
-partition: 0049	total: 450	per_label:   41	  51	  32	  41	  48	  51	  60	  49	  35	  42
-partition: 0050	total: 450	per_label:   51	  38	  36	  47	  44	  43	  46	  53	  46	  46
-partition: 0051	total: 450	per_label:   42	  54	  48	  37	  43	  45	  46	  36	  50	  49
-partition: 0052	total: 450	per_label:   44	  40	  44	  41	  43	  50	  42	  41	  50	  55
-partition: 0053	total: 450	per_label:   44	  61	  41	  47	  43	  42	  37	  49	  41	  45
-partition: 0054	total: 450	per_label:   48	  45	  43	  52	  54	  45	  32	  47	  53	  31
-partition: 0055	total: 450	per_label:   46	  35	  57	  44	  46	  54	  36	  51	  41	  40
-partition: 0056	total: 450	per_label:   35	  45	  43	  36	  35	  50	  63	  42	  49	  52
-partition: 0057	total: 450	per_label:   39	  41	  55	  45	  55	  43	  44	  49	  44	  35
-partition: 0058	total: 450	per_label:   42	  37	  51	  49	  41	  38	  46	  55	  49	  42
-partition: 0059	total: 450	per_label:   48	  45	  35	  42	  46	  52	  40	  44	  37	  61
-partition: 0060	total: 450	per_label:   52	  45	  52	  36	  48	  52	  36	  50	  38	  41
-partition: 0061	total: 450	per_label:   56	  44	  52	  53	  36	  46	  42	  39	  41	  41
-partition: 0062	total: 450	per_label:   42	  46	  40	  29	  52	  41	  52	  67	  38	  43
-partition: 0063	total: 450	per_label:   44	  35	  58	  48	  40	  47	  36	  52	  46	  44
-partition: 0064	total: 450	per_label:   48	  48	  54	  38	  46	  42	  34	  41	  55	  44
-partition: 0065	total: 450	per_label:   40	  41	  43	  39	  51	  56	  38	  50	  51	  41
-partition: 0066	total: 450	per_label:   61	  42	  51	  33	  55	  37	  42	  39	  42	  48
-partition: 0067	total: 450	per_label:   39	  40	  37	  43	  52	  53	  43	  58	  36	  49
-partition: 0068	total: 450	per_label:   45	  62	  46	  42	  39	  48	  42	  37	  49	  40
-partition: 0069	total: 450	per_label:   41	  48	  40	  49	  51	  51	  46	  34	  50	  40
-partition: 0070	total: 450	per_label:   48	  45	  49	  32	  47	  40	  40	  52	  50	  47
-partition: 0071	total: 450	per_label:   57	  40	  38	  38	  46	  57	  49	  37	  52	  36
-partition: 0072	total: 450	per_label:   43	  38	  46	  49	  50	  36	  46	  38	  53	  51
-partition: 0073	total: 450	per_label:   49	  43	  40	  48	  48	  34	  54	  55	  40	  39
-partition: 0074	total: 450	per_label:   52	  43	  45	  48	  45	  33	  47	  44	  47	  46
-partition: 0075	total: 450	per_label:   39	  42	  56	  46	  35	  49	  48	  50	  46	  39
-partition: 0076	total: 450	per_label:   42	  57	  38	  44	  42	  45	  43	  34	  51	  54
-partition: 0077	total: 450	per_label:   35	  50	  55	  44	  44	  45	  51	  40	  41	  45
-partition: 0078	total: 450	per_label:   37	  39	  41	  54	  46	  50	  51	  36	  47	  49
-partition: 0079	total: 450	per_label:   46	  48	  52	  47	  52	  39	  41	  40	  38	  47
-partition: 0080	total: 450	per_label:   38	  44	  45	  45	  36	  42	  53	  65	  39	  43
-partition: 0081	total: 450	per_label:   44	  46	  41	  52	  50	  44	  38	  48	  43	  44
-partition: 0082	total: 450	per_label:   44	  47	  49	  47	  40	  36	  56	  41	  49	  41
-partition: 0083	total: 450	per_label:   56	  30	  40	  49	  41	  49	  36	  61	  43	  45
-partition: 0084	total: 450	per_label:   37	  46	  45	  43	  44	  52	  39	  50	  48	  46
-partition: 0085	total: 450	per_label:   42	  39	  46	  40	  56	  45	  44	  43	  45	  50
-partition: 0086	total: 450	per_label:   46	  41	  46	  50	  62	  38	  36	  56	  40	  35
-partition: 0087	total: 450	per_label:   48	  47	  38	  48	  35	  55	  43	  48	  45	  43
-partition: 0088	total: 450	per_label:   41	  42	  42	  52	  36	  55	  38	  36	  62	  46
-partition: 0089	total: 450	per_label:   34	  42	  53	  43	  48	  36	  51	  54	  51	  38
-partition: 0090	total: 450	per_label:   47	  52	  48	  50	  38	  38	  30	  56	  41	  50
-partition: 0091	total: 450	per_label:   45	  48	  45	  55	  47	  34	  50	  43	  40	  43
-partition: 0092	total: 450	per_label:   42	  55	  30	  48	  38	  53	  50	  50	  40	  44
-partition: 0093	total: 450	per_label:   43	  39	  49	  47	  34	  47	  40	  43	  62	  46
-partition: 0094	total: 450	per_label:   33	  39	  46	  42	  42	  47	  59	  49	  46	  47
-partition: 0095	total: 450	per_label:   50	  35	  55	  47	  41	  37	  49	  49	  41	  46
-partition: 0096	total: 450	per_label:   50	  43	  48	  60	  33	  44	  43	  46	  40	  43
-partition: 0097	total: 450	per_label:   35	  49	  42	  47	  48	  41	  53	  38	  51	  46
-partition: 0098	total: 450	per_label:   50	  51	  40	  48	  52	  48	  44	  37	  40	  40
-partition: 0099	total: 450	per_label:   45	  43	  47	  48	  42	  47	  39	  45	  48	  46
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_005.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_005.txt
deleted file mode 100644
index 5fd8da4cb..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_005.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_005
-number_of_examples_per_label_per_shard
-partition: 0000	total: 348	per_label:   37	  30	  29	  41	  38	  40	  31	  31	  35	  36
-partition: 0001	total: 350	per_label:   37	  33	  31	  37	  42	  40	  37	  33	  23	  37
-partition: 0002	total: 351	per_label:   34	  45	  37	  40	  36	  27	  25	  34	  37	  36
-partition: 0003	total: 353	per_label:   44	  35	  35	  29	  33	  40	  35	  32	  31	  39
-partition: 0004	total: 355	per_label:   35	  41	  39	  36	  28	  25	  37	  43	  35	  36
-partition: 0005	total: 357	per_label:   26	  39	  41	  44	  30	  31	  28	  37	  46	  35
-partition: 0006	total: 358	per_label:   31	  43	  42	  33	  44	  37	  27	  30	  40	  31
-partition: 0007	total: 360	per_label:   34	  35	  41	  32	  36	  28	  41	  35	  44	  34
-partition: 0008	total: 362	per_label:   52	  26	  52	  39	  27	  37	  37	  39	  25	  28
-partition: 0009	total: 364	per_label:   38	  27	  46	  31	  34	  42	  40	  38	  30	  38
-partition: 0010	total: 366	per_label:   35	  35	  39	  38	  33	  40	  43	  30	  30	  43
-partition: 0011	total: 367	per_label:   44	  31	  33	  41	  26	  38	  27	  49	  29	  49
-partition: 0012	total: 369	per_label:   34	  39	  31	  37	  30	  38	  40	  39	  44	  37
-partition: 0013	total: 371	per_label:   35	  38	  41	  33	  41	  30	  31	  40	  43	  39
-partition: 0014	total: 373	per_label:   38	  35	  35	  38	  37	  32	  29	  48	  44	  37
-partition: 0015	total: 375	per_label:   28	  37	  39	  33	  32	  36	  33	  44	  44	  49
-partition: 0016	total: 377	per_label:   43	  37	  32	  38	  35	  34	  50	  36	  42	  30
-partition: 0017	total: 379	per_label:   35	  32	  40	  34	  47	  43	  45	  36	  37	  30
-partition: 0018	total: 381	per_label:   44	  36	  34	  35	  41	  43	  33	  30	  44	  41
-partition: 0019	total: 382	per_label:   54	  34	  26	  37	  42	  41	  39	  29	  34	  46
-partition: 0020	total: 384	per_label:   40	  33	  40	  36	  33	  36	  50	  32	  41	  43
-partition: 0021	total: 386	per_label:   41	  26	  47	  41	  33	  52	  40	  38	  36	  32
-partition: 0022	total: 388	per_label:   46	  41	  47	  45	  45	  27	  38	  26	  34	  39
-partition: 0023	total: 390	per_label:   40	  38	  38	  38	  54	  41	  35	  27	  33	  46
-partition: 0024	total: 392	per_label:   33	  40	  37	  39	  41	  32	  41	  43	  40	  46
-partition: 0025	total: 394	per_label:   37	  37	  38	  36	  52	  40	  44	  35	  44	  31
-partition: 0026	total: 396	per_label:   41	  52	  22	  44	  33	  26	  50	  39	  36	  53
-partition: 0027	total: 398	per_label:   43	  41	  53	  33	  52	  48	  38	  25	  29	  36
-partition: 0028	total: 400	per_label:   42	  37	  41	  31	  38	  36	  44	  37	  50	  44
-partition: 0029	total: 402	per_label:   49	  37	  39	  31	  43	  50	  39	  34	  35	  45
-partition: 0030	total: 404	per_label:   43	  48	  42	  38	  41	  41	  39	  41	  34	  37
-partition: 0031	total: 406	per_label:   38	  44	  47	  34	  45	  38	  49	  40	  35	  36
-partition: 0032	total: 408	per_label:   36	  36	  43	  53	  38	  36	  38	  41	  41	  46
-partition: 0033	total: 410	per_label:   44	  46	  47	  23	  32	  35	  48	  55	  38	  42
-partition: 0034	total: 412	per_label:   35	  42	  44	  40	  46	  37	  46	  37	  45	  40
-partition: 0035	total: 414	per_label:   50	  48	  45	  36	  40	  48	  44	  35	  33	  35
-partition: 0036	total: 416	per_label:   40	  40	  43	  43	  41	  43	  35	  39	  45	  47
-partition: 0037	total: 418	per_label:   52	  38	  41	  45	  46	  46	  36	  31	  48	  35
-partition: 0038	total: 421	per_label:   41	  32	  33	  46	  49	  33	  60	  40	  40	  47
-partition: 0039	total: 423	per_label:   45	  38	  41	  52	  39	  45	  48	  36	  39	  40
-partition: 0040	total: 425	per_label:   46	  46	  42	  44	  39	  49	  32	  39	  47	  41
-partition: 0041	total: 427	per_label:   38	  57	  43	  39	  35	  40	  33	  41	  43	  58
-partition: 0042	total: 429	per_label:   42	  64	  30	  52	  33	  41	  42	  45	  41	  39
-partition: 0043	total: 431	per_label:   41	  34	  37	  41	  47	  53	  48	  45	  39	  46
-partition: 0044	total: 433	per_label:   44	  56	  35	  45	  51	  46	  39	  46	  37	  34
-partition: 0045	total: 436	per_label:   46	  48	  34	  51	  44	  43	  48	  47	  41	  34
-partition: 0046	total: 438	per_label:   36	  50	  34	  46	  50	  42	  46	  46	  47	  41
-partition: 0047	total: 440	per_label:   35	  45	  46	  42	  39	  52	  44	  51	  43	  43
-partition: 0048	total: 442	per_label:   51	  47	  37	  38	  41	  47	  45	  40	  53	  43
-partition: 0049	total: 444	per_label:   35	  58	  34	  45	  47	  41	  38	  41	  59	  46
-partition: 0050	total: 447	per_label:   41	  46	  53	  38	  53	  41	  45	  39	  51	  40
-partition: 0051	total: 449	per_label:   53	  48	  47	  47	  41	  49	  40	  41	  36	  47
-partition: 0052	total: 451	per_label:   41	  42	  49	  44	  46	  42	  48	  42	  42	  55
-partition: 0053	total: 453	per_label:   52	  46	  48	  48	  39	  43	  59	  42	  40	  36
-partition: 0054	total: 456	per_label:   36	  41	  35	  56	  48	  45	  53	  47	  43	  52
-partition: 0055	total: 458	per_label:   34	  47	  36	  43	  48	  56	  57	  51	  38	  48
-partition: 0056	total: 460	per_label:   51	  38	  39	  49	  47	  45	  44	  62	  41	  44
-partition: 0057	total: 462	per_label:   48	  55	  42	  42	  40	  46	  51	  39	  50	  49
-partition: 0058	total: 465	per_label:   42	  45	  51	  41	  47	  48	  45	  41	  52	  53
-partition: 0059	total: 467	per_label:   49	  58	  42	  45	  47	  45	  40	  50	  42	  49
-partition: 0060	total: 469	per_label:   47	  47	  45	  56	  54	  47	  32	  49	  57	  35
-partition: 0061	total: 472	per_label:   49	  40	  58	  45	  46	  58	  38	  53	  44	  41
-partition: 0062	total: 474	per_label:   38	  45	  46	  40	  39	  51	  65	  45	  50	  55
-partition: 0063	total: 476	per_label:   40	  43	  62	  47	  58	  45	  50	  49	  45	  37
-partition: 0064	total: 479	per_label:   41	  42	  46	  53	  49	  40	  44	  62	  55	  47
-partition: 0065	total: 481	per_label:   61	  49	  39	  43	  49	  58	  41	  44	  41	  56
-partition: 0066	total: 484	per_label:   51	  45	  66	  47	  43	  53	  39	  52	  41	  47
-partition: 0067	total: 486	per_label:   53	  54	  45	  48	  45	  47	  51	  54	  42	  47
-partition: 0068	total: 489	per_label:   48	  43	  60	  34	  54	  44	  54	  63	  38	  51
-partition: 0069	total: 491	per_label:   52	  37	  57	  55	  50	  49	  34	  50	  59	  48
-partition: 0070	total: 493	per_label:   47	  59	  52	  35	  42	  60	  44	  48	  61	  45
-partition: 0071	total: 496	per_label:   65	  34	  52	  42	  67	  44	  40	  54	  46	  52
-partition: 0072	total: 498	per_label:   44	  53	  43	  44	  55	  59	  51	  57	  42	  50
-partition: 0073	total: 501	per_label:   51	  66	  52	  46	  47	  52	  43	  44	  54	  46
-partition: 0074	total: 503	per_label:   47	  53	  41	  57	  57	  56	  55	  40	  53	  44
-partition: 0075	total: 506	per_label:   53	  53	  55	  33	  51	  52	  47	  55	  57	  50
-partition: 0076	total: 508	per_label:   61	  43	  46	  45	  57	  62	  47	  40	  59	  48
-partition: 0077	total: 511	per_label:   49	  47	  49	  57	  48	  34	  66	  54	  56	  51
-partition: 0078	total: 514	per_label:   60	  48	  55	  51	  56	  35	  58	  56	  48	  47
-partition: 0079	total: 516	per_label:   51	  45	  56	  60	  45	  52	  48	  54	  54	  51
-partition: 0080	total: 519	per_label:   48	  64	  47	  46	  48	  53	  52	  43	  59	  59
-partition: 0081	total: 521	per_label:   40	  59	  59	  54	  51	  52	  60	  46	  46	  54
-partition: 0082	total: 524	per_label:   47	  45	  46	  65	  55	  58	  58	  43	  55	  52
-partition: 0083	total: 526	per_label:   52	  53	  61	  51	  49	  50	  48	  63	  43	  56
-partition: 0084	total: 529	per_label:   44	  55	  47	  55	  59	  47	  52	  64	  52	  54
-partition: 0085	total: 532	per_label:   53	  56	  62	  58	  46	  44	  64	  47	  54	  48
-partition: 0086	total: 534	per_label:   66	  39	  49	  59	  50	  55	  45	  69	  51	  51
-partition: 0087	total: 537	per_label:   48	  50	  52	  55	  51	  62	  44	  58	  59	  58
-partition: 0088	total: 540	per_label:   46	  53	  55	  46	  74	  55	  50	  55	  49	  57
-partition: 0089	total: 542	per_label:   53	  54	  53	  55	  59	  49	  50	  70	  56	  43
-partition: 0090	total: 545	per_label:   54	  52	  49	  65	  41	  67	  52	  44	  65	  56
-partition: 0091	total: 548	per_label:   47	  49	  60	  56	  56	  52	  56	  64	  62	  46
-partition: 0092	total: 551	per_label:   54	  63	  55	  64	  49	  46	  43	  65	  55	  57
-partition: 0093	total: 553	per_label:   56	  70	  53	  61	  51	  45	  51	  62	  46	  58
-partition: 0094	total: 556	per_label:   51	  51	  44	  64	  46	  64	  61	  52	  65	  58
-partition: 0095	total: 559	per_label:   48	  47	  66	  49	  49	  59	  69	  56	  61	  55
-partition: 0096	total: 562	per_label:   59	  44	  65	  62	  53	  46	  63	  61	  51	  58
-partition: 0097	total: 565	per_label:   60	  54	  60	  63	  49	  59	  62	  52	  48	  58
-partition: 0098	total: 567	per_label:   50	  63	  48	  71	  63	  51	  56	  49	  69	  47
-partition: 0099	total: 570	per_label:   61	  60	  59	  57	  54	  62	  50	  55	  54	  58
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_010.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_010.txt
deleted file mode 100644
index 17bcb2a92..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_010.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_010
-number_of_examples_per_label_per_shard
-partition: 0000	total: 264	per_label:   29	  24	  24	  30	  28	  31	  23	  27	  23	  25
-partition: 0001	total: 266	per_label:   30	  25	  16	  30	  28	  35	  29	  23	  20	  30
-partition: 0002	total: 269	per_label:   23	  29	  30	  31	  34	  21	  23	  22	  24	  32
-partition: 0003	total: 272	per_label:   29	  33	  31	  28	  28	  22	  20	  27	  29	  25
-partition: 0004	total: 275	per_label:   34	  24	  25	  23	  27	  30	  29	  27	  26	  30
-partition: 0005	total: 277	per_label:   27	  30	  31	  26	  18	  24	  29	  34	  26	  32
-partition: 0006	total: 280	per_label:   25	  37	  34	  28	  31	  21	  25	  25	  28	  26
-partition: 0007	total: 283	per_label:   23	  30	  28	  42	  22	  23	  23	  30	  40	  22
-partition: 0008	total: 286	per_label:   24	  34	  35	  22	  35	  33	  19	  25	  31	  28
-partition: 0009	total: 289	per_label:   30	  31	  35	  26	  26	  22	  30	  29	  34	  26
-partition: 0010	total: 291	per_label:   30	  22	  42	  31	  29	  28	  34	  21	  28	  26
-partition: 0011	total: 294	per_label:   43	  16	  35	  23	  21	  38	  34	  40	  16	  28
-partition: 0012	total: 297	per_label:   31	  26	  32	  28	  34	  32	  28	  23	  30	  33
-partition: 0013	total: 300	per_label:   28	  30	  36	  34	  20	  28	  37	  29	  23	  35
-partition: 0014	total: 303	per_label:   34	  26	  29	  33	  24	  30	  25	  42	  23	  37
-partition: 0015	total: 306	per_label:   37	  31	  27	  35	  24	  29	  29	  32	  33	  29
-partition: 0016	total: 309	per_label:   21	  35	  35	  27	  29	  35	  27	  34	  38	  28
-partition: 0017	total: 312	per_label:   30	  24	  20	  29	  36	  24	  26	  43	  40	  40
-partition: 0018	total: 316	per_label:   29	  31	  40	  35	  29	  25	  23	  37	  33	  34
-partition: 0019	total: 319	per_label:   26	  32	  32	  23	  27	  35	  31	  37	  37	  39
-partition: 0020	total: 322	per_label:   41	  32	  24	  34	  30	  26	  44	  29	  35	  27
-partition: 0021	total: 325	per_label:   28	  29	  27	  29	  42	  35	  39	  33	  35	  28
-partition: 0022	total: 328	per_label:   36	  33	  34	  25	  36	  42	  34	  22	  36	  30
-partition: 0023	total: 332	per_label:   51	  26	  29	  34	  34	  33	  27	  31	  30	  37
-partition: 0024	total: 335	per_label:   39	  26	  26	  34	  33	  41	  39	  21	  33	  43
-partition: 0025	total: 338	per_label:   30	  32	  41	  36	  30	  26	  44	  26	  38	  35
-partition: 0026	total: 342	per_label:   38	  24	  38	  35	  30	  45	  33	  37	  33	  29
-partition: 0027	total: 345	per_label:   41	  38	  43	  40	  41	  25	  35	  24	  27	  31
-partition: 0028	total: 349	per_label:   37	  33	  35	  31	  49	  36	  29	  26	  31	  42
-partition: 0029	total: 352	per_label:   25	  34	  33	  40	  36	  32	  33	  39	  38	  42
-partition: 0030	total: 356	per_label:   35	  38	  35	  31	  49	  28	  45	  31	  34	  30
-partition: 0031	total: 359	per_label:   39	  38	  23	  37	  31	  32	  45	  35	  38	  41
-partition: 0032	total: 363	per_label:   38	  47	  32	  35	  41	  34	  36	  28	  27	  45
-partition: 0033	total: 367	per_label:   39	  29	  46	  28	  45	  40	  40	  27	  40	  33
-partition: 0034	total: 370	per_label:   41	  37	  44	  29	  32	  46	  37	  29	  38	  37
-partition: 0035	total: 374	per_label:   43	  38	  30	  29	  44	  38	  38	  41	  30	  43
-partition: 0036	total: 378	per_label:   44	  45	  43	  33	  33	  37	  38	  37	  36	  32
-partition: 0037	total: 381	per_label:   32	  37	  47	  42	  37	  36	  41	  32	  30	  47
-partition: 0038	total: 385	per_label:   31	  38	  43	  41	  43	  32	  40	  47	  39	  31
-partition: 0039	total: 389	per_label:   43	  44	  38	  27	  29	  34	  47	  47	  36	  44
-partition: 0040	total: 393	per_label:   35	  43	  42	  36	  44	  33	  43	  38	  42	  37
-partition: 0041	total: 397	per_label:   48	  43	  44	  34	  41	  49	  40	  31	  33	  34
-partition: 0042	total: 401	per_label:   39	  39	  42	  41	  39	  41	  34	  38	  45	  43
-partition: 0043	total: 405	per_label:   50	  38	  41	  43	  43	  42	  36	  32	  44	  36
-partition: 0044	total: 409	per_label:   42	  29	  30	  44	  47	  35	  59	  37	  38	  48
-partition: 0045	total: 413	per_label:   42	  38	  35	  57	  40	  41	  45	  35	  41	  39
-partition: 0046	total: 417	per_label:   46	  43	  46	  38	  38	  53	  36	  37	  45	  35
-partition: 0047	total: 421	per_label:   37	  59	  41	  40	  37	  39	  29	  43	  39	  57
-partition: 0048	total: 426	per_label:   42	  57	  33	  51	  30	  37	  40	  42	  47	  47
-partition: 0049	total: 430	per_label:   39	  40	  37	  46	  41	  52	  52	  47	  34	  42
-partition: 0050	total: 434	per_label:   45	  55	  33	  40	  54	  51	  39	  46	  39	  32
-partition: 0051	total: 438	per_label:   43	  44	  38	  51	  50	  41	  45	  41	  45	  40
-partition: 0052	total: 443	per_label:   43	  55	  33	  51	  48	  40	  46	  54	  41	  32
-partition: 0053	total: 447	per_label:   31	  44	  45	  41	  41	  56	  45	  48	  47	  49
-partition: 0054	total: 452	per_label:   54	  51	  38	  38	  43	  44	  44	  41	  56	  43
-partition: 0055	total: 456	per_label:   36	  57	  35	  47	  46	  47	  40	  44	  56	  48
-partition: 0056	total: 461	per_label:   43	  49	  52	  39	  54	  39	  48	  41	  54	  42
-partition: 0057	total: 465	per_label:   53	  49	  52	  47	  42	  52	  41	  42	  39	  48
-partition: 0058	total: 470	per_label:   43	  42	  51	  47	  50	  43	  52	  41	  45	  56
-partition: 0059	total: 475	per_label:   54	  48	  47	  49	  39	  48	  63	  46	  43	  38
-partition: 0060	total: 480	per_label:   35	  40	  42	  61	  50	  49	  51	  57	  39	  56
-partition: 0061	total: 484	per_label:   41	  54	  35	  45	  53	  56	  61	  54	  38	  47
-partition: 0062	total: 489	per_label:   58	  44	  45	  50	  45	  45	  49	  55	  54	  44
-partition: 0063	total: 494	per_label:   44	  60	  47	  42	  50	  47	  50	  47	  49	  58
-partition: 0064	total: 499	per_label:   52	  45	  51	  42	  46	  55	  51	  42	  53	  62
-partition: 0065	total: 504	per_label:   51	  63	  41	  57	  54	  51	  34	  55	  55	  43
-partition: 0066	total: 509	per_label:   54	  48	  61	  57	  53	  56	  37	  57	  47	  39
-partition: 0067	total: 514	per_label:   45	  46	  55	  43	  50	  59	  61	  47	  55	  53
-partition: 0068	total: 519	per_label:   40	  48	  59	  51	  55	  53	  56	  56	  52	  49
-partition: 0069	total: 525	per_label:   47	  42	  59	  57	  54	  42	  55	  64	  58	  47
-partition: 0070	total: 530	per_label:   59	  55	  41	  50	  55	  64	  46	  51	  47	  62
-partition: 0071	total: 535	per_label:   59	  51	  72	  52	  46	  56	  43	  59	  46	  51
-partition: 0072	total: 541	per_label:   59	  59	  47	  47	  52	  55	  56	  65	  48	  53
-partition: 0073	total: 546	per_label:   57	  44	  69	  49	  57	  54	  52	  61	  48	  55
-partition: 0074	total: 551	per_label:   57	  57	  66	  49	  52	  54	  42	  56	  67	  51
-partition: 0075	total: 557	per_label:   55	  48	  55	  46	  68	  57	  49	  60	  59	  60
-partition: 0076	total: 562	per_label:   65	  49	  55	  49	  66	  53	  55	  62	  54	  54
-partition: 0077	total: 568	per_label:   54	  71	  50	  50	  58	  69	  57	  55	  53	  51
-partition: 0078	total: 574	per_label:   54	  65	  54	  63	  59	  65	  53	  43	  64	  54
-partition: 0079	total: 580	per_label:   63	  60	  61	  40	  59	  59	  56	  62	  64	  56
-partition: 0080	total: 585	per_label:   65	  49	  55	  55	  72	  60	  54	  48	  68	  59
-partition: 0081	total: 591	per_label:   64	  54	  55	  66	  57	  45	  73	  66	  56	  55
-partition: 0082	total: 597	per_label:   68	  55	  65	  61	  56	  45	  60	  67	  63	  57
-partition: 0083	total: 603	per_label:   56	  64	  62	  57	  50	  70	  62	  49	  65	  68
-partition: 0084	total: 609	per_label:   46	  73	  66	  65	  60	  57	  69	  54	  60	  59
-partition: 0085	total: 615	per_label:   53	  55	  54	  74	  64	  67	  71	  51	  61	  65
-partition: 0086	total: 621	per_label:   59	  66	  74	  60	  58	  53	  57	  76	  55	  63
-partition: 0087	total: 628	per_label:   62	  61	  59	  72	  66	  60	  62	  68	  58	  60
-partition: 0088	total: 634	per_label:   59	  56	  62	  67	  63	  55	  69	  74	  68	  61
-partition: 0089	total: 640	per_label:   68	  60	  63	  61	  58	  74	  54	  72	  65	  65
-partition: 0090	total: 647	per_label:   59	  60	  65	  61	  82	  68	  57	  64	  60	  71
-partition: 0091	total: 653	per_label:   68	  62	  62	  72	  69	  63	  63	  78	  62	  54
-partition: 0092	total: 660	per_label:   64	  62	  63	  78	  61	  74	  57	  56	  83	  62
-partition: 0093	total: 666	per_label:   60	  74	  74	  58	  59	  59	  62	  87	  70	  63
-partition: 0094	total: 673	per_label:   65	  71	  62	  82	  61	  57	  64	  74	  67	  70
-partition: 0095	total: 680	per_label:   63	  74	  61	  78	  55	  75	  68	  64	  72	  70
-partition: 0096	total: 686	per_label:   65	  51	  78	  57	  68	  64	  89	  72	  73	  69
-partition: 0097	total: 693	per_label:   69	  66	  78	  95	  51	  67	  68	  66	  62	  71
-partition: 0098	total: 700	per_label:   63	  74	  58	  75	  75	  62	  80	  66	  76	  71
-partition: 0099	total: 707	per_label:   76	  73	  76	  72	  71	  79	  62	  67	  67	  64
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_015.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_015.txt
deleted file mode 100644
index 9bda3b73e..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_015.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_015
-number_of_examples_per_label_per_shard
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-partition: 0001	total: 199	per_label:   20	  16	  13	  19	  20	  20	  21	  23	  21	  26
-partition: 0002	total: 202	per_label:   24	  19	  13	  23	  22	  29	  19	  18	  14	  21
-partition: 0003	total: 205	per_label:   18	  24	  25	  23	  28	  12	  19	  15	  17	  24
-partition: 0004	total: 208	per_label:   22	  27	  22	  19	  23	  15	  13	  23	  27	  17
-partition: 0005	total: 212	per_label:   27	  18	  20	  20	  18	  27	  18	  20	  18	  26
-partition: 0006	total: 215	per_label:   25	  22	  23	  20	  18	  20	  26	  19	  19	  23
-partition: 0007	total: 218	per_label:   20	  24	  25	  21	  17	  15	  23	  29	  20	  24
-partition: 0008	total: 221	per_label:   21	  29	  24	  23	  26	  17	  19	  18	  23	  21
-partition: 0009	total: 225	per_label:   15	  25	  24	  30	  13	  20	  16	  29	  33	  20
-partition: 0010	total: 228	per_label:   19	  28	  26	  25	  29	  24	  18	  16	  22	  21
-partition: 0011	total: 231	per_label:   22	  20	  31	  20	  28	  20	  23	  21	  27	  19
-partition: 0012	total: 235	per_label:   21	  27	  26	  18	  20	  23	  25	  27	  28	  20
-partition: 0013	total: 238	per_label:   29	  18	  34	  27	  23	  21	  26	  15	  21	  24
-partition: 0014	total: 242	per_label:   40	  15	  29	  22	  12	  27	  22	  37	  16	  22
-partition: 0015	total: 246	per_label:   21	  18	  35	  23	  24	  28	  30	  21	  22	  24
-partition: 0016	total: 249	per_label:   27	  25	  22	  23	  27	  31	  29	  18	  22	  25
-partition: 0017	total: 253	per_label:   23	  26	  32	  28	  19	  23	  25	  28	  18	  31
-partition: 0018	total: 257	per_label:   32	  20	  21	  29	  18	  23	  19	  37	  22	  36
-partition: 0019	total: 261	per_label:   30	  26	  25	  31	  22	  25	  25	  26	  27	  24
-partition: 0020	total: 265	per_label:   17	  30	  31	  25	  24	  31	  25	  29	  31	  22
-partition: 0021	total: 269	per_label:   27	  22	  20	  21	  31	  18	  24	  36	  33	  37
-partition: 0022	total: 273	per_label:   32	  30	  24	  28	  27	  22	  25	  27	  35	  23
-partition: 0023	total: 277	per_label:   14	  23	  33	  29	  26	  27	  17	  36	  31	  41
-partition: 0024	total: 281	per_label:   27	  22	  29	  26	  25	  30	  33	  33	  27	  29
-partition: 0025	total: 285	per_label:   35	  36	  18	  25	  24	  24	  38	  28	  38	  19
-partition: 0026	total: 289	per_label:   27	  24	  25	  23	  40	  34	  35	  26	  28	  27
-partition: 0027	total: 294	per_label:   31	  30	  32	  25	  31	  36	  31	  19	  32	  27
-partition: 0028	total: 298	per_label:   48	  24	  25	  30	  34	  28	  25	  26	  28	  30
-partition: 0029	total: 303	per_label:   31	  25	  20	  29	  32	  35	  30	  26	  29	  46
-partition: 0030	total: 307	per_label:   33	  29	  36	  31	  23	  27	  42	  23	  34	  29
-partition: 0031	total: 312	per_label:   33	  22	  37	  38	  28	  36	  34	  27	  29	  28
-partition: 0032	total: 317	per_label:   40	  28	  42	  28	  33	  34	  28	  28	  26	  30
-partition: 0033	total: 321	per_label:   33	  33	  37	  42	  42	  24	  34	  16	  28	  32
-partition: 0034	total: 326	per_label:   28	  39	  26	  26	  39	  38	  26	  29	  34	  41
-partition: 0035	total: 331	per_label:   28	  26	  32	  36	  38	  27	  38	  40	  30	  36
-partition: 0036	total: 336	per_label:   35	  34	  33	  28	  44	  32	  37	  28	  38	  27
-partition: 0037	total: 341	per_label:   37	  38	  18	  39	  30	  24	  44	  37	  31	  43
-partition: 0038	total: 346	per_label:   37	  43	  37	  33	  40	  37	  36	  23	  25	  35
-partition: 0039	total: 351	per_label:   36	  29	  43	  24	  41	  40	  36	  26	  42	  34
-partition: 0040	total: 357	per_label:   40	  37	  43	  29	  31	  39	  34	  31	  37	  36
-partition: 0041	total: 362	per_label:   43	  35	  30	  27	  42	  37	  37	  38	  29	  44
-partition: 0042	total: 367	per_label:   40	  44	  40	  33	  34	  36	  39	  37	  34	  30
-partition: 0043	total: 373	per_label:   32	  37	  45	  41	  36	  35	  40	  30	  30	  47
-partition: 0044	total: 379	per_label:   30	  38	  41	  40	  43	  32	  40	  46	  38	  31
-partition: 0045	total: 384	per_label:   44	  43	  40	  26	  28	  33	  44	  48	  36	  42
-partition: 0046	total: 390	per_label:   32	  43	  43	  36	  44	  35	  42	  34	  43	  38
-partition: 0047	total: 396	per_label:   47	  41	  42	  35	  39	  46	  44	  35	  33	  34
-partition: 0048	total: 402	per_label:   43	  42	  41	  41	  39	  42	  33	  35	  45	  41
-partition: 0049	total: 408	per_label:   49	  38	  40	  42	  45	  42	  37	  34	  42	  39
-partition: 0050	total: 414	per_label:   41	  29	  34	  43	  47	  37	  57	  38	  40	  48
-partition: 0051	total: 420	per_label:   44	  38	  35	  60	  40	  41	  47	  35	  41	  39
-partition: 0052	total: 427	per_label:   46	  44	  48	  40	  39	  53	  36	  39	  45	  37
-partition: 0053	total: 433	per_label:   38	  60	  44	  42	  37	  39	  30	  41	  43	  59
-partition: 0054	total: 439	per_label:   43	  61	  30	  50	  32	  40	  45	  48	  47	  43
-partition: 0055	total: 446	per_label:   44	  39	  38	  45	  48	  57	  48	  44	  37	  46
-partition: 0056	total: 453	per_label:   44	  59	  37	  45	  55	  48	  41	  48	  40	  36
-partition: 0057	total: 460	per_label:   50	  48	  33	  55	  46	  47	  52	  48	  43	  38
-partition: 0058	total: 466	per_label:   37	  54	  38	  47	  54	  48	  51	  51	  46	  40
-partition: 0059	total: 473	per_label:   41	  51	  48	  45	  42	  48	  39	  54	  51	  54
-partition: 0060	total: 481	per_label:   48	  59	  36	  43	  47	  49	  50	  49	  61	  39
-partition: 0061	total: 488	per_label:   40	  50	  46	  43	  47	  56	  46	  40	  63	  57
-partition: 0062	total: 495	per_label:   53	  50	  67	  54	  57	  40	  48	  35	  47	  44
-partition: 0063	total: 503	per_label:   57	  59	  46	  48	  41	  53	  47	  49	  50	  53
-partition: 0064	total: 510	per_label:   53	  44	  58	  45	  56	  43	  67	  52	  41	  51
-partition: 0065	total: 518	per_label:   41	  47	  40	  68	  47	  56	  61	  52	  48	  58
-partition: 0066	total: 526	per_label:   40	  56	  43	  52	  58	  61	  62	  60	  43	  51
-partition: 0067	total: 533	per_label:   63	  46	  44	  56	  52	  51	  57	  66	  48	  50
-partition: 0068	total: 541	per_label:   50	  66	  54	  45	  55	  53	  52	  50	  57	  59
-partition: 0069	total: 550	per_label:   54	  56	  55	  47	  53	  57	  54	  46	  59	  69
-partition: 0070	total: 558	per_label:   61	  61	  49	  64	  59	  54	  41	  62	  64	  43
-partition: 0071	total: 566	per_label:   57	  49	  70	  62	  61	  66	  43	  61	  50	  47
-partition: 0072	total: 575	per_label:   48	  56	  66	  44	  44	  65	  73	  58	  63	  58
-partition: 0073	total: 583	per_label:   48	  53	  60	  64	  66	  52	  60	  66	  60	  54
-partition: 0074	total: 592	per_label:   61	  53	  54	  58	  62	  61	  53	  63	  52	  75
-partition: 0075	total: 601	per_label:   70	  59	  70	  56	  58	  68	  50	  64	  55	  51
-partition: 0076	total: 610	per_label:   62	  66	  59	  60	  56	  63	  62	  68	  55	  59
-partition: 0077	total: 619	per_label:   66	  49	  76	  53	  67	  59	  55	  77	  57	  60
-partition: 0078	total: 628	per_label:   62	  64	  72	  56	  61	  69	  53	  62	  73	  56
-partition: 0079	total: 638	per_label:   76	  50	  66	  51	  80	  56	  57	  63	  67	  72
-partition: 0080	total: 647	per_label:   62	  71	  54	  60	  72	  72	  63	  76	  56	  61
-partition: 0081	total: 657	per_label:   62	  79	  64	  65	  62	  75	  62	  56	  71	  61
-partition: 0082	total: 667	per_label:   66	  71	  72	  52	  73	  66	  63	  64	  76	  64
-partition: 0083	total: 677	per_label:   77	  59	  63	  62	  75	  76	  64	  56	  79	  66
-partition: 0084	total: 687	per_label:   78	  67	  62	  77	  71	  49	  84	  76	  61	  62
-partition: 0085	total: 698	per_label:   71	  62	  77	  74	  60	  61	  73	  73	  76	  71
-partition: 0086	total: 708	per_label:   64	  81	  66	  67	  64	  77	  72	  60	  80	  77
-partition: 0087	total: 719	per_label:   55	  72	  81	  80	  74	  71	  82	  58	  72	  74
-partition: 0088	total: 729	per_label:   73	  71	  76	  77	  71	  74	  71	  83	  59	  74
-partition: 0089	total: 740	per_label:   68	  77	  74	  83	  77	  65	  75	  81	  70	  70
-partition: 0090	total: 751	per_label:   77	  65	  73	  80	  69	  67	  78	  87	  80	  75
-partition: 0091	total: 763	per_label:   77	  73	  76	  71	  72	  86	  67	  78	  77	  86
-partition: 0092	total: 774	per_label:   71	  69	  82	  77	 107	  75	  67	  94	  69	  63
-partition: 0093	total: 786	per_label:   79	  79	  65	  89	  60	  95	  73	  72	  96	  78
-partition: 0094	total: 798	per_label:   74	  84	  89	  76	  80	  67	  75	  95	  85	  73
-partition: 0095	total: 810	per_label:   78	  94	  77	  99	  73	  68	  74	  90	  72	  85
-partition: 0096	total: 822	per_label:   70	  72	  78	  85	  66	  90	 101	  78	 101	  81
-partition: 0097	total: 834	per_label:   84	  69	  95	  87	  80	  77	  90	  91	  74	  87
-partition: 0098	total: 847	per_label:   82	  88	  80	 101	  78	  81	  90	  81	  84	  82
-partition: 0099	total: 859	per_label:   89	  90	  87	  91	  88	  91	  78	  75	  88	  82
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_020.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_020.txt
deleted file mode 100644
index a224d688c..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_020.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_020
-number_of_examples_per_label_per_shard
-partition: 0000	total: 144	per_label:   16	  16	  15	  17	  18	  17	  12	  13	   8	  12
-partition: 0001	total: 147	per_label:   16	  12	  12	  14	  12	  16	  14	  15	  18	  18
-partition: 0002	total: 150	per_label:   15	  13	   8	  19	  16	  21	  17	  14	  11	  16
-partition: 0003	total: 153	per_label:   16	  13	  11	  18	  17	  21	  14	  13	  13	  17
-partition: 0004	total: 156	per_label:   17	  17	  20	  15	  22	   9	  18	  12	  13	  13
-partition: 0005	total: 159	per_label:   11	  15	  17	  18	  18	  13	   9	  16	  21	  21
-partition: 0006	total: 162	per_label:   20	  25	  18	  18	  15	  12	  11	  16	  12	  15
-partition: 0007	total: 165	per_label:   20	  13	  13	  14	  15	  22	  12	  17	  19	  20
-partition: 0008	total: 169	per_label:   22	  19	  18	  15	  16	  16	  22	  14	  11	  16
-partition: 0009	total: 172	per_label:   14	  13	  21	  18	   9	  11	  23	  26	  20	  17
-partition: 0010	total: 175	per_label:   18	  28	  18	  16	  17	  14	  13	  17	  15	  19
-partition: 0011	total: 179	per_label:   13	  22	  22	  21	  20	  13	  15	  14	  21	  18
-partition: 0012	total: 182	per_label:   15	  17	  18	  24	  12	  18	  13	  23	  25	  17
-partition: 0013	total: 186	per_label:   15	  24	  22	  21	  22	  17	  13	  13	  21	  18
-partition: 0014	total: 190	per_label:   18	  21	  24	  12	  23	  22	  15	  17	  23	  15
-partition: 0015	total: 194	per_label:   21	  18	  26	  21	  19	  11	  23	  18	  22	  15
-partition: 0016	total: 198	per_label:   17	  23	  18	  15	  22	  20	  20	  22	  21	  20
-partition: 0017	total: 202	per_label:   26	  12	  33	  24	  14	  20	  21	  17	  16	  19
-partition: 0018	total: 206	per_label:   33	  10	  23	  17	  13	  26	  22	  29	  14	  19
-partition: 0019	total: 210	per_label:   18	  16	  28	  18	  20	  24	  26	  19	  19	  22
-partition: 0020	total: 214	per_label:   26	  24	  22	  16	  26	  25	  25	  11	  16	  23
-partition: 0021	total: 218	per_label:   17	  19	  26	  30	  15	  20	  24	  26	  17	  24
-partition: 0022	total: 223	per_label:   25	  16	  24	  21	  17	  21	  19	  35	  17	  28
-partition: 0023	total: 227	per_label:   30	  19	  16	  31	  16	  24	  13	  26	  21	  31
-partition: 0024	total: 232	per_label:   20	  28	  20	  22	  19	  23	  29	  19	  31	  21
-partition: 0025	total: 236	per_label:   19	  26	  32	  22	  22	  24	  20	  28	  26	  17
-partition: 0026	total: 241	per_label:   23	  21	  17	  20	  28	  19	  20	  33	  27	  33
-partition: 0027	total: 246	per_label:   30	  27	  21	  24	  26	  17	  24	  23	  33	  21
-partition: 0028	total: 251	per_label:   12	  19	  31	  28	  24	  26	  14	  36	  26	  35
-partition: 0029	total: 256	per_label:   24	  24	  27	  23	  19	  27	  26	  31	  26	  29
-partition: 0030	total: 261	per_label:   36	  29	  18	  26	  26	  20	  34	  21	  30	  21
-partition: 0031	total: 266	per_label:   20	  24	  23	  19	  31	  29	  32	  34	  32	  22
-partition: 0032	total: 271	per_label:   27	  22	  31	  27	  31	  34	  35	   9	  30	  25
-partition: 0033	total: 277	per_label:   34	  29	  24	  25	  30	  30	  21	  27	  28	  29
-partition: 0034	total: 282	per_label:   44	  25	  20	  22	  34	  32	  28	  23	  23	  31
-partition: 0035	total: 288	per_label:   32	  21	  25	  31	  24	  32	  33	  22	  30	  38
-partition: 0036	total: 294	per_label:   28	  28	  36	  34	  27	  22	  40	  20	  31	  28
-partition: 0037	total: 300	per_label:   32	  20	  33	  28	  24	  43	  33	  35	  27	  25
-partition: 0038	total: 306	per_label:   37	  28	  41	  35	  34	  22	  26	  21	  29	  33
-partition: 0039	total: 312	per_label:   36	  33	  34	  35	  43	  23	  32	  20	  25	  31
-partition: 0040	total: 318	per_label:   22	  39	  27	  31	  38	  38	  24	  28	  35	  36
-partition: 0041	total: 324	per_label:   29	  29	  31	  30	  36	  26	  37	  41	  28	  37
-partition: 0042	total: 331	per_label:   36	  32	  27	  31	  43	  35	  39	  25	  37	  26
-partition: 0043	total: 338	per_label:   33	  42	  20	  35	  30	  21	  44	  33	  31	  49
-partition: 0044	total: 344	per_label:   41	  39	  40	  33	  43	  39	  32	  22	  25	  30
-partition: 0045	total: 351	per_label:   35	  31	  40	  28	  37	  36	  40	  28	  40	  36
-partition: 0046	total: 358	per_label:   41	  34	  41	  26	  31	  44	  35	  31	  40	  35
-partition: 0047	total: 365	per_label:   43	  33	  36	  28	  45	  35	  36	  41	  27	  41
-partition: 0048	total: 373	per_label:   40	  44	  39	  34	  38	  36	  37	  36	  35	  34
-partition: 0049	total: 380	per_label:   32	  41	  44	  40	  36	  37	  42	  28	  33	  47
-partition: 0050	total: 388	per_label:   30	  37	  45	  41	  36	  33	  41	  55	  37	  33
-partition: 0051	total: 396	per_label:   42	  46	  40	  28	  33	  36	  47	  43	  40	  41
-partition: 0052	total: 403	per_label:   38	  43	  41	  35	  50	  33	  48	  38	  40	  37
-partition: 0053	total: 412	per_label:   53	  46	  44	  43	  35	  50	  34	  33	  37	  37
-partition: 0054	total: 420	per_label:   40	  39	  42	  41	  44	  45	  45	  36	  40	  48
-partition: 0055	total: 428	per_label:   52	  37	  45	  40	  48	  40	  36	  38	  54	  38
-partition: 0056	total: 437	per_label:   41	  35	  32	  60	  48	  41	  59	  41	  39	  41
-partition: 0057	total: 446	per_label:   48	  46	  48	  46	  39	  52	  46	  34	  43	  44
-partition: 0058	total: 454	per_label:   51	  47	  46	  45	  44	  45	  32	  44	  48	  52
-partition: 0059	total: 464	per_label:   42	  63	  37	  55	  35	  34	  41	  44	  50	  63
-partition: 0060	total: 473	per_label:   40	  53	  37	  50	  45	  57	  54	  56	  35	  46
-partition: 0061	total: 482	per_label:   50	  62	  40	  42	  55	  62	  43	  48	  45	  35
-partition: 0062	total: 492	per_label:   50	  48	  41	  59	  52	  47	  53	  47	  51	  44
-partition: 0063	total: 502	per_label:   45	  62	  37	  53	  55	  48	  52	  58	  50	  42
-partition: 0064	total: 512	per_label:   43	  52	  52	  47	  47	  59	  48	  59	  51	  54
-partition: 0065	total: 522	per_label:   52	  65	  36	  49	  50	  51	  52	  51	  71	  45
-partition: 0066	total: 533	per_label:   46	  57	  52	  47	  61	  58	  49	  42	  62	  59
-partition: 0067	total: 543	per_label:   56	  52	  71	  57	  54	  52	  54	  41	  51	  55
-partition: 0068	total: 554	per_label:   59	  59	  55	  52	  53	  52	  55	  61	  47	  61
-partition: 0069	total: 565	per_label:   63	  53	  55	  62	  48	  53	  73	  54	  57	  47
-partition: 0070	total: 576	per_label:   36	  54	  45	  68	  64	  66	  70	  68	  42	  63
-partition: 0071	total: 588	per_label:   62	  56	  52	  61	  61	  57	  61	  62	  57	  59
-partition: 0072	total: 600	per_label:   62	  69	  53	  50	  55	  59	  68	  59	  62	  63
-partition: 0073	total: 612	per_label:   63	  60	  64	  53	  60	  63	  56	  57	  64	  72
-partition: 0074	total: 624	per_label:   64	  74	  53	  69	  68	  62	  45	  66	  69	  54
-partition: 0075	total: 637	per_label:   60	  55	  76	  65	  67	  75	  52	  72	  59	  56
-partition: 0076	total: 649	per_label:   57	  65	  76	  52	  60	  68	  80	  59	  71	  61
-partition: 0077	total: 662	per_label:   56	  53	  72	  77	  68	  58	  68	  82	  67	  61
-partition: 0078	total: 676	per_label:   79	  70	  54	  61	  71	  79	  61	  66	  59	  76
-partition: 0079	total: 689	per_label:   80	  69	  86	  72	  57	  71	  58	  68	  61	  67
-partition: 0080	total: 703	per_label:   65	  64	  80	  55	  79	  62	  76	  92	  58	  72
-partition: 0081	total: 717	per_label:   73	  69	  81	  67	  69	  83	  51	  75	  86	  63
-partition: 0082	total: 731	per_label:   85	  58	  74	  60	  88	  69	  64	  77	  76	  80
-partition: 0083	total: 746	per_label:   70	  88	  64	  67	  78	  82	  79	  82	  67	  69
-partition: 0084	total: 761	per_label:   74	  86	  72	  80	  81	  86	  71	  58	  82	  71
-partition: 0085	total: 776	per_label:   88	  75	  79	  55	  79	  84	  75	  76	  90	  75
-partition: 0086	total: 792	per_label:   80	  71	  75	  83	  82	  66	  92	  77	  87	  79
-partition: 0087	total: 807	per_label:   89	  73	  88	  86	  82	  64	  77	  89	  83	  76
-partition: 0088	total: 824	per_label:   76	  93	  78	  81	  71	  89	  88	  72	  87	  89
-partition: 0089	total: 840	per_label:   67	  82	  87	  99	  86	  86	  97	  70	  78	  88
-partition: 0090	total: 857	per_label:   79	  91	  94	  87	  89	  74	  85	 102	  73	  83
-partition: 0091	total: 874	per_label:   85	  89	  90	  93	  83	  79	  93	  86	  92	  84
-partition: 0092	total: 891	per_label:   92	  75	  83	  90	  85	 101	  75	 110	  91	  89
-partition: 0093	total: 909	per_label:   89	  81	  94	  92	 117	  84	  80	 100	  85	  87
-partition: 0094	total: 928	per_label:   90	  91	  85	 103	  77	 112	  83	  85	 111	  91
-partition: 0095	total: 946	per_label:   84	  99	  98	  98	  91	  81	  90	 117	  96	  92
-partition: 0096	total: 965	per_label:   99	 112	  87	 106	  82	  87	 101	 102	  90	  99
-partition: 0097	total: 984	per_label:   88	  77	 113	  99	  93	  99	 111	 100	 110	  94
-partition: 0098	total: 1004	per_label:   97	  99	 107	 112	  87	  98	 108	  96	  94	 106
-partition: 0099	total: 1024	per_label:  105	 107	  93	 117	 105	 105	  96	  94	 106	  96
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_025.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_025.txt
deleted file mode 100644
index 0d7a3d270..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_025.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_025
-number_of_examples_per_label_per_shard
-partition: 0000	total: 104	per_label:   14	  13	  11	  10	  12	  12	   7	  10	   5	  10
-partition: 0001	total: 106	per_label:    8	   7	  11	  18	  10	  16	  12	   7	  11	   6
-partition: 0002	total: 109	per_label:   11	   9	   6	   7	  11	   8	  10	  12	  17	  18
-partition: 0003	total: 112	per_label:   12	  11	   7	  15	  11	  17	  12	  11	   4	  12
-partition: 0004	total: 114	per_label:   16	  10	   6	  10	  16	  15	  12	  10	   9	  10
-partition: 0005	total: 117	per_label:    8	  11	  13	  16	  17	  10	  10	  10	  10	  12
-partition: 0006	total: 120	per_label:   13	  13	  14	  12	  11	   8	  12	  11	  10	  16
-partition: 0007	total: 123	per_label:    9	  11	  15	  12	  14	  11	   8	  12	  18	  13
-partition: 0008	total: 126	per_label:   15	  23	  12	  16	  13	   7	   7	  13	  10	  10
-partition: 0009	total: 130	per_label:   16	   8	  11	   7	  12	  19	  14	  11	  14	  18
-partition: 0010	total: 133	per_label:   18	  10	  16	  14	  12	  13	  11	  17	  10	  12
-partition: 0011	total: 136	per_label:   15	  19	  13	  13	  11	  12	  18	   8	  12	  15
-partition: 0012	total: 140	per_label:   12	  10	  18	  15	   7	  10	  18	  22	  15	  13
-partition: 0013	total: 143	per_label:   13	  24	  13	   9	  14	  13	  10	  16	  13	  18
-partition: 0014	total: 147	per_label:   16	  17	  18	  18	  22	   7	  11	  10	  14	  14
-partition: 0015	total: 150	per_label:    7	  18	  19	  17	   7	  14	  14	  18	  23	  13
-partition: 0016	total: 154	per_label:   14	  16	  16	  27	  15	  12	  10	  16	  18	  10
-partition: 0017	total: 158	per_label:   14	  19	  16	  12	  19	  20	  12	  12	  16	  18
-partition: 0018	total: 162	per_label:   14	  19	  22	  12	  17	  18	  13	  14	  20	  13
-partition: 0019	total: 166	per_label:   17	  13	  23	  18	  18	  11	  21	  16	  16	  13
-partition: 0020	total: 170	per_label:   14	  17	  14	  12	  17	  15	  18	  19	  26	  18
-partition: 0021	total: 174	per_label:   23	  17	  28	  19	  16	  17	  15	  13	  11	  15
-partition: 0022	total: 179	per_label:   28	  11	  23	  20	   8	  19	  21	  24	  11	  14
-partition: 0023	total: 183	per_label:   22	  10	  22	  11	  18	  24	  24	  19	  12	  21
-partition: 0024	total: 188	per_label:   18	  17	  24	  20	  18	  19	  16	  20	  19	  17
-partition: 0025	total: 193	per_label:   21	  20	  18	  17	  20	  23	  24	   9	  18	  23
-partition: 0026	total: 197	per_label:   16	  17	  24	  25	  15	  18	  22	  22	  15	  23
-partition: 0027	total: 202	per_label:   23	  15	  22	  18	  17	  20	  17	  31	  15	  24
-partition: 0028	total: 207	per_label:   30	  18	  17	  26	  14	  21	  11	  27	  16	  27
-partition: 0029	total: 213	per_label:   21	  25	  18	  25	  17	  18	  27	  16	  28	  18
-partition: 0030	total: 218	per_label:   13	  27	  22	  20	  20	  28	  19	  27	  22	  20
-partition: 0031	total: 223	per_label:   20	  20	  22	  20	  28	  14	  17	  22	  32	  28
-partition: 0032	total: 229	per_label:   22	  21	  18	  20	  21	  20	  20	  32	  28	  27
-partition: 0033	total: 235	per_label:   22	  21	  29	  26	  24	  19	  18	  26	  25	  25
-partition: 0034	total: 241	per_label:   18	  26	  23	  21	  20	  28	  22	  28	  30	  25
-partition: 0035	total: 247	per_label:   30	  13	  26	  26	  23	  21	  29	  30	  20	  29
-partition: 0036	total: 253	per_label:   27	  35	  15	  21	  21	  22	  35	  25	  35	  17
-partition: 0037	total: 259	per_label:   23	  20	  25	  22	  37	  29	  31	  23	  24	  25
-partition: 0038	total: 266	per_label:   26	  24	  31	  22	  27	  35	  27	  17	  31	  26
-partition: 0039	total: 272	per_label:   39	  22	  21	  30	  31	  29	  23	  27	  22	  28
-partition: 0040	total: 279	per_label:   38	  30	  21	  21	  28	  28	  29	  20	  30	  34
-partition: 0041	total: 286	per_label:   29	  20	  28	  28	  25	  31	  32	  24	  34	  35
-partition: 0042	total: 293	per_label:   31	  26	  36	  37	  28	  25	  36	  21	  28	  25
-partition: 0043	total: 301	per_label:   36	  21	  33	  29	  27	  41	  32	  29	  25	  28
-partition: 0044	total: 308	per_label:   33	  35	  37	  36	  36	  22	  30	  23	  27	  29
-partition: 0045	total: 316	per_label:   32	  30	  35	  28	  44	  30	  29	  23	  28	  37
-partition: 0046	total: 324	per_label:   26	  36	  26	  34	  37	  33	  30	  30	  31	  41
-partition: 0047	total: 332	per_label:   28	  29	  37	  30	  42	  23	  38	  38	  35	  32
-partition: 0048	total: 340	per_label:   39	  35	  26	  33	  34	  38	  40	  32	  33	  30
-partition: 0049	total: 349	per_label:   36	  47	  23	  39	  35	  23	  38	  29	  30	  49
-partition: 0050	total: 357	per_label:   40	  38	  45	  28	  44	  43	  38	  19	  33	  29
-partition: 0051	total: 366	per_label:   37	  32	  40	  27	  36	  35	  42	  34	  44	  39
-partition: 0052	total: 376	per_label:   47	  34	  36	  31	  38	  45	  35	  34	  32	  44
-partition: 0053	total: 385	per_label:   40	  46	  41	  36	  41	  35	  39	  40	  31	  36
-partition: 0054	total: 395	per_label:   40	  40	  45	  31	  45	  40	  47	  38	  36	  33
-partition: 0055	total: 405	per_label:   34	  41	  41	  50	  36	  39	  38	  40	  39	  47
-partition: 0056	total: 415	per_label:   42	  45	  49	  30	  34	  35	  45	  53	  39	  43
-partition: 0057	total: 425	per_label:   39	  44	  45	  39	  45	  37	  51	  40	  47	  38
-partition: 0058	total: 436	per_label:   52	  48	  47	  38	  44	  51	  44	  38	  34	  40
-partition: 0059	total: 447	per_label:   42	  41	  46	  46	  44	  49	  40	  41	  47	  51
-partition: 0060	total: 458	per_label:   56	  45	  45	  46	  52	  41	  42	  39	  54	  38
-partition: 0061	total: 469	per_label:   45	  36	  36	  61	  50	  43	  64	  44	  44	  46
-partition: 0062	total: 481	per_label:   54	  48	  51	  51	  42	  55	  50	  38	  46	  46
-partition: 0063	total: 493	per_label:   54	  56	  48	  46	  47	  50	  35	  50	  48	  59
-partition: 0064	total: 505	per_label:   42	  69	  40	  59	  37	  43	  45	  50	  56	  64
-partition: 0065	total: 518	per_label:   51	  53	  43	  53	  52	  67	  57	  51	  44	  47
-partition: 0066	total: 531	per_label:   49	  70	  40	  53	  70	  57	  45	  57	  48	  42
-partition: 0067	total: 544	per_label:   56	  59	  39	  65	  49	  51	  67	  60	  54	  44
-partition: 0068	total: 558	per_label:   42	  57	  58	  53	  58	  66	  54	  60	  57	  53
-partition: 0069	total: 572	per_label:   65	  61	  50	  51	  51	  57	  57	  58	  65	  57
-partition: 0070	total: 586	per_label:   46	  71	  47	  56	  60	  60	  54	  54	  75	  63
-partition: 0071	total: 601	per_label:   61	  59	  79	  60	  69	  54	  54	  44	  62	  59
-partition: 0072	total: 616	per_label:   65	  64	  59	  59	  60	  60	  65	  63	  53	  68
-partition: 0073	total: 631	per_label:   65	  60	  60	  68	  51	  62	  79	  66	  67	  53
-partition: 0074	total: 647	per_label:   48	  65	  51	  73	  74	  71	  74	  72	  43	  76
-partition: 0075	total: 663	per_label:   76	  61	  60	  66	  65	  65	  74	  74	  66	  56
-partition: 0076	total: 680	per_label:   65	  75	  60	  65	  66	  72	  66	  60	  70	  81
-partition: 0077	total: 697	per_label:   66	  86	  71	  64	  67	  67	  59	  69	  75	  73
-partition: 0078	total: 714	per_label:   74	  62	  84	  78	  81	  77	  56	  80	  68	  54
-partition: 0079	total: 732	per_label:   66	  69	  79	  61	  64	  82	  84	  70	  83	  74
-partition: 0080	total: 750	per_label:   61	  62	  79	  82	  87	  66	  79	  90	  73	  71
-partition: 0081	total: 769	per_label:   88	  78	  74	  68	  72	  93	  67	  77	  66	  86
-partition: 0082	total: 788	per_label:   85	  81	  83	  79	  72	  78	  75	  88	  71	  76
-partition: 0083	total: 808	per_label:   82	  64	  97	  71	  87	  77	  72	  97	  78	  83
-partition: 0084	total: 828	per_label:   84	  85	  93	  70	  91	  89	  63	  84	  96	  73
-partition: 0085	total: 849	per_label:   95	  81	  76	  73	  97	  88	  86	  91	  72	  90
-partition: 0086	total: 870	per_label:   82	 104	  82	  87	  86	  96	  85	  71	  99	  78
-partition: 0087	total: 892	per_label:  103	  85	  89	  69	  92	  96	  88	  86	 100	  84
-partition: 0088	total: 914	per_label:   93	  82	  86	  99	 100	  73	 101	  96	  94	  90
-partition: 0089	total: 937	per_label:   94	  92	 105	  96	  83	  84	  99	  96	 101	  87
-partition: 0090	total: 961	per_label:   84	 111	  98	  96	  93	  97	 102	  81	  90	 109
-partition: 0091	total: 985	per_label:   91	  94	  96	 109	  98	 102	 102	  98	  95	 100
-partition: 0092	total: 1009	per_label:   94	 102	 106	 109	 101	  88	 102	 112	  97	  98
-partition: 0093	total: 1035	per_label:  106	  91	  99	 106	  99	 108	  96	 119	 108	 103
-partition: 0094	total: 1061	per_label:  102	  97	 110	 106	 130	 107	  93	 116	 100	 100
-partition: 0095	total: 1087	per_label:  103	 105	  98	 116	  96	 117	 108	 109	 133	 102
-partition: 0096	total: 1114	per_label:  109	 120	 119	 125	 100	  96	  96	 131	 102	 116
-partition: 0097	total: 1142	per_label:  101	 111	 112	 118	  97	 117	 130	 113	 131	 112
-partition: 0098	total: 1171	per_label:  121	 104	 123	 133	 101	 116	 127	 116	 103	 127
-partition: 0099	total: 1200	per_label:  117	 130	 116	 130	 129	 117	 117	 111	 125	 108
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_030.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_030.txt
deleted file mode 100644
index 39054dd51..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_030.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_030
-number_of_examples_per_label_per_shard
-partition: 0000	total: 74	per_label:   11	   7	   8	   7	  12	   6	   5	   8	   4	   6
-partition: 0001	total: 76	per_label:    6	   9	   7	  12	   6	  11	   7	   6	   5	   7
-partition: 0002	total: 78	per_label:    6	   4	   7	  10	   7	  12	   9	   6	   9	   8
-partition: 0003	total: 81	per_label:   10	   9	   5	   4	   7	   7	   8	   8	  12	  11
-partition: 0004	total: 83	per_label:    7	   6	   6	  12	   9	  10	   9	   9	   6	   9
-partition: 0005	total: 86	per_label:   12	  10	   5	  10	   9	  13	   9	   7	   3	   8
-partition: 0006	total: 88	per_label:    9	   8	   5	   8	  12	  10	   7	   6	  10	  13
-partition: 0007	total: 91	per_label:    7	   8	  10	  13	  14	   9	   9	  10	   6	   5
-partition: 0008	total: 93	per_label:   12	  10	  13	   7	   9	   6	  12	   7	   8	   9
-partition: 0009	total: 96	per_label:    5	   8	   8	  11	  12	   6	   3	  16	  10	  17
-partition: 0010	total: 99	per_label:   10	  13	  12	  10	  12	   7	   8	   6	  15	   6
-partition: 0011	total: 102	per_label:   13	  16	  11	  14	   7	   9	   7	   8	   7	  10
-partition: 0012	total: 105	per_label:   14	   7	   9	   4	  11	  15	  10	   8	  11	  16
-partition: 0013	total: 108	per_label:   11	   9	  13	  12	   9	  13	   6	  14	  11	  10
-partition: 0014	total: 112	per_label:   18	  13	   8	  12	  10	   8	  16	   8	   7	  12
-partition: 0015	total: 115	per_label:    9	  15	  14	  10	   6	   9	  15	  17	  11	   9
-partition: 0016	total: 119	per_label:    9	  10	  14	  12	  10	   9	  13	  16	  12	  14
-partition: 0017	total: 122	per_label:   13	  20	  13	  10	  11	  11	   9	  10	  12	  13
-partition: 0018	total: 126	per_label:   14	  13	  15	  14	  19	   7	  10	   9	  12	  13
-partition: 0019	total: 130	per_label:    5	  17	  19	  15	   7	  12	   9	  15	  19	  12
-partition: 0020	total: 133	per_label:   13	  12	  10	  20	   9	  13	  12	  16	  17	  11
-partition: 0021	total: 138	per_label:   10	  16	  17	  17	  17	  11	  12	  10	  15	  13
-partition: 0022	total: 142	per_label:   13	  16	  17	   8	  18	  21	  12	   8	  16	  13
-partition: 0023	total: 146	per_label:   15	  13	  18	  14	  17	  10	  14	  16	  16	  13
-partition: 0024	total: 150	per_label:   16	  19	  19	  15	  11	   9	  13	  14	  24	  10
-partition: 0025	total: 155	per_label:   10	  15	  14	  11	  17	  17	  19	  19	  14	  19
-partition: 0026	total: 159	per_label:   21	  13	  28	  20	  17	  15	  13	  11	  10	  11
-partition: 0027	total: 164	per_label:   27	  10	  20	  17	   5	  18	  19	  24	  11	  13
-partition: 0028	total: 169	per_label:   21	   9	  21	  11	  15	  23	  21	  18	  12	  18
-partition: 0029	total: 174	per_label:   16	  13	  23	  17	  16	  17	  18	  17	  18	  19
-partition: 0030	total: 180	per_label:   23	  20	  15	  14	  25	  20	  20	   8	  14	  21
-partition: 0031	total: 185	per_label:   14	  15	  23	  23	  11	  17	  22	  25	  16	  19
-partition: 0032	total: 191	per_label:   22	  20	  19	  18	  15	  23	  16	  22	  12	  24
-partition: 0033	total: 196	per_label:   22	  17	  18	  25	  13	  15	  16	  25	  18	  27
-partition: 0034	total: 202	per_label:   23	  15	  20	  27	  17	  18	  15	  27	  20	  20
-partition: 0035	total: 208	per_label:   15	  25	  17	  15	  18	  27	  26	  17	  27	  21
-partition: 0036	total: 215	per_label:   17	  27	  27	  19	  21	  19	  18	  24	  27	  16
-partition: 0037	total: 221	per_label:   24	  15	  15	  20	  25	  20	  16	  31	  24	  31
-partition: 0038	total: 228	per_label:   29	  25	  21	  24	  23	  13	  24	  21	  30	  18
-partition: 0039	total: 234	per_label:   10	  19	  29	  26	  22	  24	  14	  34	  25	  31
-partition: 0040	total: 241	per_label:   22	  24	  24	  18	  19	  27	  23	  28	  26	  30
-partition: 0041	total: 249	per_label:   32	  20	  22	  29	  25	  19	  34	  20	  27	  21
-partition: 0042	total: 256	per_label:   22	  29	  19	  16	  24	  29	  31	  34	  31	  21
-partition: 0043	total: 264	per_label:   26	  23	  29	  27	  35	  31	  34	  12	  26	  21
-partition: 0044	total: 272	per_label:   34	  26	  23	  26	  31	  32	  21	  24	  26	  29
-partition: 0045	total: 280	per_label:   42	  23	  22	  24	  28	  27	  28	  24	  30	  32
-partition: 0046	total: 288	per_label:   35	  24	  21	  29	  30	  32	  31	  19	  29	  38
-partition: 0047	total: 297	per_label:   27	  29	  33	  31	  27	  24	  40	  23	  32	  31
-partition: 0048	total: 306	per_label:   33	  21	  37	  34	  27	  39	  32	  32	  26	  25
-partition: 0049	total: 315	per_label:   37	  28	  45	  28	  32	  30	  31	  27	  27	  30
-partition: 0050	total: 325	per_label:   37	  35	  32	  44	  44	  24	  30	  17	  29	  33
-partition: 0051	total: 334	per_label:   25	  38	  31	  27	  41	  39	  28	  30	  35	  40
-partition: 0052	total: 345	per_label:   30	  32	  33	  34	  37	  27	  38	  43	  33	  38
-partition: 0053	total: 355	per_label:   37	  35	  29	  34	  46	  38	  42	  30	  36	  28
-partition: 0054	total: 366	per_label:   39	  46	  25	  41	  34	  21	  44	  30	  34	  52
-partition: 0055	total: 377	per_label:   44	  39	  48	  29	  46	  46	  38	  24	  28	  35
-partition: 0056	total: 388	per_label:   38	  35	  41	  30	  37	  36	  43	  37	  49	  42
-partition: 0057	total: 399	per_label:   48	  38	  38	  31	  43	  50	  38	  34	  35	  44
-partition: 0058	total: 411	per_label:   44	  50	  44	  38	  41	  40	  40	  42	  35	  37
-partition: 0059	total: 424	per_label:   37	  41	  51	  37	  44	  41	  50	  40	  37	  46
-partition: 0060	total: 437	per_label:   39	  42	  48	  51	  44	  37	  44	  51	  42	  39
-partition: 0061	total: 450	per_label:   46	  51	  47	  32	  34	  43	  53	  53	  44	  47
-partition: 0062	total: 463	per_label:   43	  52	  45	  41	  57	  38	  56	  43	  47	  41
-partition: 0063	total: 477	per_label:   60	  44	  54	  50	  44	  52	  35	  39	  48	  51
-partition: 0064	total: 491	per_label:   51	  54	  49	  49	  48	  56	  49	  39	  46	  50
-partition: 0065	total: 506	per_label:   55	  34	  43	  51	  57	  46	  64	  49	  54	  53
-partition: 0066	total: 521	per_label:   55	  48	  49	  64	  51	  55	  57	  45	  52	  45
-partition: 0067	total: 537	per_label:   57	  60	  56	  54	  51	  57	  39	  49	  57	  57
-partition: 0068	total: 553	per_label:   48	  79	  46	  63	  42	  46	  46	  53	  57	  73
-partition: 0069	total: 570	per_label:   56	  57	  46	  56	  57	  73	  65	  62	  48	  50
-partition: 0070	total: 587	per_label:   59	  77	  41	  63	  72	  62	  49	  61	  54	  49
-partition: 0071	total: 604	per_label:   62	  66	  54	  71	  58	  52	  70	  68	  58	  45
-partition: 0072	total: 623	per_label:   48	  63	  60	  53	  62	  76	  62	  68	  63	  68
-partition: 0073	total: 641	per_label:   64	  80	  46	  63	  57	  61	  61	  61	  87	  61
-partition: 0074	total: 661	per_label:   60	  68	  75	  57	  78	  64	  68	  53	  73	  65
-partition: 0075	total: 680	per_label:   76	  74	  71	  72	  57	  73	  61	  60	  65	  71
-partition: 0076	total: 701	per_label:   70	  61	  75	  72	  73	  65	  90	  71	  58	  66
-partition: 0077	total: 722	per_label:   52	  74	  58	  83	  73	  80	  78	  84	  59	  81
-partition: 0078	total: 744	per_label:   84	  68	  64	  71	  76	  71	  88	  81	  75	  66
-partition: 0079	total: 766	per_label:   70	  81	  75	  69	  75	  81	  74	  67	  81	  93
-partition: 0080	total: 789	per_label:   85	  92	  71	  79	  80	  78	  62	  82	  85	  75
-partition: 0081	total: 813	per_label:   77	  69	  90	  86	  88	  94	  76	  87	  74	  72
-partition: 0082	total: 837	per_label:   68	  77	 100	  79	  84	  83	  95	  86	  88	  77
-partition: 0083	total: 862	per_label:   89	  83	  76	  86	  88	  89	  77	  95	  84	  95
-partition: 0084	total: 888	per_label:  106	  91	 105	  83	  73	  90	  79	  94	  80	  87
-partition: 0085	total: 915	per_label:   89	  78	 101	  79	 101	  92	  88	 112	  80	  95
-partition: 0086	total: 942	per_label:   97	  90	 104	  81	 101	  95	  79	  95	 110	  90
-partition: 0087	total: 970	per_label:  101	 104	  88	  85	 107	 103	  96	 107	  89	  90
-partition: 0088	total: 999	per_label:  101	 108	 101	  97	 107	 107	  90	  87	 108	  93
-partition: 0089	total: 1029	per_label:  109	  93	 100	  92	 108	 106	 105	  90	 121	 105
-partition: 0090	total: 1060	per_label:  119	 100	 108	 109	 105	  81	 115	 121	 103	  99
-partition: 0091	total: 1092	per_label:   90	 121	 112	 110	  99	 117	 121	  92	 115	 115
-partition: 0092	total: 1125	per_label:  106	 113	 117	 123	 115	 117	 114	 103	 103	 114
-partition: 0093	total: 1159	per_label:  106	 118	 118	 125	 111	  98	 125	 130	 115	 113
-partition: 0094	total: 1194	per_label:  124	 103	 114	 121	 114	 127	 104	 136	 121	 130
-partition: 0095	total: 1229	per_label:  124	 116	 119	 131	 139	 128	 110	 139	 120	 103
-partition: 0096	total: 1266	per_label:  110	 128	 132	 135	 117	 121	 115	 137	 142	 129
-partition: 0097	total: 1304	per_label:  129	 140	 121	 144	 114	 128	 132	 131	 138	 127
-partition: 0098	total: 1343	per_label:  131	 116	 148	 150	 116	 127	 150	 140	 129	 136
-partition: 0099	total: 1384	per_label:  133	 145	 133	 145	 145	 139	 141	 128	 140	 135
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_035.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_035.txt
deleted file mode 100644
index b8a5dc001..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_035.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_035
-number_of_examples_per_label_per_shard
-partition: 0000	total: 52	per_label:    7	   6	   4	   4	  11	   4	   2	   6	   4	   4
-partition: 0001	total: 54	per_label:    7	   7	   8	   7	   1	   8	   5	   4	   1	   6
-partition: 0002	total: 55	per_label:    3	   3	   5	  10	   7	   6	   8	   4	   6	   3
-partition: 0003	total: 57	per_label:    5	   4	   5	   8	   4	  11	   5	   3	   7	   5
-partition: 0004	total: 59	per_label:   10	   5	   3	   2	   6	   2	   4	  11	   6	  10
-partition: 0005	total: 62	per_label:    3	   5	   4	   7	   8	   8	   6	   3	  11	   7
-partition: 0006	total: 64	per_label:    5	   5	   5	   9	   6	   8	   9	   8	   1	   8
-partition: 0007	total: 66	per_label:   10	   9	   4	   7	   7	  10	   6	   5	   3	   5
-partition: 0008	total: 68	per_label:   11	   5	   2	   6	   8	  10	   7	   6	   6	   7
-partition: 0009	total: 71	per_label:    4	   7	   6	  10	   7	  10	   6	   8	   5	   8
-partition: 0010	total: 73	per_label:    6	   5	  10	   8	  13	   1	   9	   6	   8	   7
-partition: 0011	total: 76	per_label:   10	  10	  12	   6	   7	   6	   8	   4	   5	   8
-partition: 0012	total: 78	per_label:    4	   7	   5	  10	  10	   6	   1	  13	   8	  14
-partition: 0013	total: 81	per_label:    8	   8	  10	   7	   8	   7	  10	   3	  14	   6
-partition: 0014	total: 84	per_label:   10	  18	   7	   7	   9	   5	   3	  11	   9	   5
-partition: 0015	total: 87	per_label:   10	   7	  11	  13	   6	  11	   7	   5	   5	  12
-partition: 0016	total: 90	per_label:   10	   8	   8	   5	  11	  11	   8	   9	  10	  10
-partition: 0017	total: 93	per_label:   12	   5	  11	  10	   8	  11	   6	  11	   9	  10
-partition: 0018	total: 97	per_label:   14	  12	   7	   9	   9	   7	  15	   7	   5	  12
-partition: 0019	total: 100	per_label:   10	  13	  12	   9	   6	   7	  13	  12	  10	   8
-partition: 0020	total: 103	per_label:    8	   6	  14	  11	   5	   9	  13	  15	  12	  10
-partition: 0021	total: 107	per_label:   11	  19	   9	   8	  13	   8	   6	  10	   8	  15
-partition: 0022	total: 111	per_label:   13	  12	  13	  12	  11	   7	  12	  10	  12	   9
-partition: 0023	total: 115	per_label:    6	  16	  16	  11	  13	  11	   9	  11	  11	  11
-partition: 0024	total: 119	per_label:    8	  14	  12	  16	   7	   9	   8	  13	  22	  10
-partition: 0025	total: 123	per_label:   10	  12	  14	  23	  12	  10	   7	  14	  11	  10
-partition: 0026	total: 127	per_label:   11	  15	  14	   9	  13	  14	  13	   9	  16	  13
-partition: 0027	total: 132	per_label:   11	  16	  16	  10	  18	  18	  11	   7	  13	  12
-partition: 0028	total: 136	per_label:   15	  10	  16	  12	  15	  10	  14	  15	  16	  13
-partition: 0029	total: 141	per_label:   15	  16	  20	  15	  11	   8	  13	  15	  19	   9
-partition: 0030	total: 146	per_label:   10	  15	  11	  10	  17	  15	  17	  17	  18	  16
-partition: 0031	total: 151	per_label:   20	  14	  26	  17	  12	  14	  13	  12	  10	  13
-partition: 0032	total: 157	per_label:   25	   9	  19	  18	  11	  15	  16	  19	  11	  14
-partition: 0033	total: 162	per_label:   22	   8	  20	  11	  11	  23	  20	  20	  12	  15
-partition: 0034	total: 168	per_label:   15	  11	  21	  15	  17	  21	  20	  17	  12	  19
-partition: 0035	total: 174	per_label:   19	  18	  18	  16	  23	  19	  16	  10	  19	  16
-partition: 0036	total: 180	per_label:   15	  18	  19	  21	  12	  19	  23	  20	  12	  21
-partition: 0037	total: 186	per_label:   19	  20	  22	  20	  13	  18	  20	  17	  15	  22
-partition: 0038	total: 193	per_label:   25	  15	  20	  19	  14	  15	  16	  29	  15	  25
-partition: 0039	total: 199	per_label:   24	  16	  15	  28	  15	  21	   9	  24	  19	  28
-partition: 0040	total: 206	per_label:   18	  23	  18	  18	  17	  22	  27	  18	  28	  17
-partition: 0041	total: 213	per_label:   17	  24	  30	  21	  20	  23	  19	  25	  21	  13
-partition: 0042	total: 221	per_label:   21	  21	  15	  18	  26	  15	  19	  25	  28	  33
-partition: 0043	total: 229	per_label:   24	  22	  20	  23	  23	  20	  19	  28	  27	  23
-partition: 0044	total: 237	per_label:   16	  20	  30	  25	  24	  18	  17	  34	  27	  26
-partition: 0045	total: 245	per_label:   21	  28	  20	  20	  20	  28	  22	  25	  31	  30
-partition: 0046	total: 254	per_label:   30	  19	  26	  29	  25	  23	  31	  26	  21	  24
-partition: 0047	total: 263	per_label:   28	  30	  18	  18	  21	  25	  38	  32	  33	  20
-partition: 0048	total: 272	per_label:   24	  22	  32	  26	  38	  31	  31	  18	  28	  22
-partition: 0049	total: 281	per_label:   32	  28	  21	  23	  33	  35	  27	  22	  30	  30
-partition: 0050	total: 291	per_label:   47	  24	  27	  28	  30	  27	  24	  24	  28	  32
-partition: 0051	total: 301	per_label:   31	  25	  22	  31	  31	  34	  34	  22	  30	  41
-partition: 0052	total: 312	per_label:   32	  29	  33	  32	  27	  27	  41	  23	  36	  32
-partition: 0053	total: 323	per_label:   33	  22	  40	  34	  28	  42	  33	  34	  30	  27
-partition: 0054	total: 334	per_label:   42	  29	  45	  35	  35	  29	  33	  26	  26	  34
-partition: 0055	total: 346	per_label:   38	  38	  37	  37	  47	  27	  35	  23	  29	  35
-partition: 0056	total: 358	per_label:   27	  41	  30	  35	  44	  41	  28	  33	  37	  42
-partition: 0057	total: 371	per_label:   30	  33	  37	  36	  44	  28	  44	  41	  39	  39
-partition: 0058	total: 384	per_label:   45	  38	  29	  37	  39	  38	  45	  35	  41	  37
-partition: 0059	total: 397	per_label:   43	  53	  32	  41	  43	  32	  45	  30	  26	  52
-partition: 0060	total: 411	per_label:   44	  34	  50	  32	  50	  48	  41	  31	  47	  34
-partition: 0061	total: 425	per_label:   48	  47	  46	  34	  34	  49	  45	  35	  45	  42
-partition: 0062	total: 440	per_label:   49	  42	  42	  33	  56	  42	  46	  47	  32	  51
-partition: 0063	total: 456	per_label:   43	  49	  53	  40	  49	  44	  51	  46	  43	  38
-partition: 0064	total: 472	per_label:   43	  47	  47	  62	  40	  43	  44	  45	  46	  55
-partition: 0065	total: 488	per_label:   49	  55	  58	  32	  38	  44	  57	  62	  45	  48
-partition: 0066	total: 505	per_label:   45	  58	  49	  46	  61	  42	  61	  46	  51	  46
-partition: 0067	total: 523	per_label:   61	  51	  58	  51	  50	  56	  41	  44	  56	  55
-partition: 0068	total: 541	per_label:   65	  50	  58	  55	  54	  64	  49	  45	  53	  48
-partition: 0069	total: 560	per_label:   53	  44	  43	  69	  63	  48	  75	  53	  56	  56
-partition: 0070	total: 580	per_label:   64	  58	  61	  60	  52	  66	  57	  43	  62	  57
-partition: 0071	total: 600	per_label:   56	  77	  55	  59	  52	  58	  47	  62	  58	  76
-partition: 0072	total: 621	per_label:   59	  74	  49	  72	  58	  61	  62	  69	  53	  64
-partition: 0073	total: 643	per_label:   64	  75	  52	  63	  70	  77	  62	  65	  61	  54
-partition: 0074	total: 665	per_label:   69	  75	  47	  77	  72	  63	  75	  70	  63	  54
-partition: 0075	total: 689	per_label:   54	  73	  71	  65	  65	  79	  66	  76	  72	  68
-partition: 0076	total: 713	per_label:   72	  85	  53	  69	  66	  70	  67	  67	  92	  72
-partition: 0077	total: 738	per_label:   69	  71	  87	  68	  84	  73	  74	  58	  80	  74
-partition: 0078	total: 764	per_label:   78	  80	  79	  74	  78	  74	  74	  76	  68	  83
-partition: 0079	total: 790	per_label:   75	  74	  71	  90	  71	  78	  99	  80	  72	  80
-partition: 0080	total: 818	per_label:   72	  80	  71	  84	  90	  91	  88	  96	  69	  77
-partition: 0081	total: 847	per_label:   87	  94	  78	  76	  79	  83	  93	  84	  84	  89
-partition: 0082	total: 876	per_label:   87	 102	  83	  87	  84	  87	  74	  81	  97	  94
-partition: 0083	total: 907	per_label:   93	  75	  96	  94	 103	 103	  75	 100	  89	  79
-partition: 0084	total: 939	per_label:   77	  87	 111	  90	  91	  94	 110	  96	  97	  86
-partition: 0085	total: 972	per_label:  106	  95	  87	  91	  99	 102	  91	 106	  92	 103
-partition: 0086	total: 1006	per_label:  105	  99	 110	  92	  96	 103	  94	 119	  88	 100
-partition: 0087	total: 1041	per_label:  110	  97	 130	  97	  99	 102	  85	 110	 111	 100
-partition: 0088	total: 1077	per_label:  116	  93	 107	  93	 128	 103	  98	 119	 110	 110
-partition: 0089	total: 1115	per_label:  104	 130	 101	 109	 114	 134	 109	  94	 115	 105
-partition: 0090	total: 1154	per_label:  126	 113	 115	  93	 126	 125	 111	 110	 127	 108
-partition: 0091	total: 1195	per_label:  135	 109	 120	 127	 120	  83	 133	 131	 120	 117
-partition: 0092	total: 1237	per_label:  104	 135	 127	 124	 111	 134	 134	 110	 130	 128
-partition: 0093	total: 1280	per_label:  117	 129	 135	 140	 128	 121	 129	 129	 119	 133
-partition: 0094	total: 1325	per_label:  127	 122	 131	 147	 132	 122	 135	 144	 136	 129
-partition: 0095	total: 1371	per_label:  134	 130	 138	 133	 155	 146	 121	 151	 129	 134
-partition: 0096	total: 1419	per_label:  136	 134	 135	 155	 132	 149	 139	 147	 160	 132
-partition: 0097	total: 1469	per_label:  145	 166	 137	 161	 132	 135	 141	 165	 138	 149
-partition: 0098	total: 1520	per_label:  141	 127	 175	 159	 132	 148	 163	 159	 164	 152
-partition: 0099	total: 1573	per_label:  158	 166	 148	 174	 156	 158	 158	 142	 158	 155
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_040.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_040.txt
deleted file mode 100644
index 604570640..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_040.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_040
-number_of_examples_per_label_per_shard
-partition: 0000	total: 36	per_label:    5	   4	   3	   2	   9	   3	   1	   3	   3	   3
-partition: 0001	total: 37	per_label:    6	   3	   5	   5	   3	   3	   4	   5	   1	   2
-partition: 0002	total: 39	per_label:    4	   6	   4	   4	   1	   7	   2	   3	   2	   6
-partition: 0003	total: 40	per_label:    2	   3	   4	   8	   5	   4	   6	   3	   3	   2
-partition: 0004	total: 42	per_label:    3	   2	   4	   7	   2	   9	   4	   2	   6	   3
-partition: 0005	total: 44	per_label:    5	   5	   3	   3	   5	   4	   4	   6	   4	   5
-partition: 0006	total: 46	per_label:    7	   3	   4	   2	   4	   2	   3	   6	   6	   9
-partition: 0007	total: 47	per_label:    2	   4	   2	   4	   7	   6	   6	   2	  10	   4
-partition: 0008	total: 49	per_label:    5	   4	   3	   9	   5	   5	   6	   4	   1	   7
-partition: 0009	total: 51	per_label:    6	   6	   3	   6	   3	  10	   5	   6	   1	   5
-partition: 0010	total: 53	per_label:    9	   5	   3	   6	   7	   6	   7	   5	   2	   3
-partition: 0011	total: 56	per_label:    7	   4	   2	   4	   7	   9	   4	   5	   7	   7
-partition: 0012	total: 58	per_label:    3	   5	   6	   8	   5	   8	   5	   7	   4	   7
-partition: 0013	total: 60	per_label:    4	   7	   8	   8	  13	   2	   6	   3	   5	   4
-partition: 0014	total: 63	per_label:    9	   6	   9	   5	   5	   5	   7	   5	   4	   8
-partition: 0015	total: 65	per_label:    5	   9	   6	   7	   7	   3	   5	   7	   7	   9
-partition: 0016	total: 68	per_label:    3	   3	   6	   7	  10	   6	   4	  11	   8	  10
-partition: 0017	total: 70	per_label:   10	   8	   8	   6	   6	   5	   7	   3	  12	   5
-partition: 0018	total: 73	per_label:    8	  16	   7	   7	   8	   3	   3	   9	   8	   4
-partition: 0019	total: 76	per_label:    9	   8	  11	  11	   6	  10	   6	   4	   2	   9
-partition: 0020	total: 79	per_label:   10	   4	   5	   3	   9	  11	   8	   6	  10	  13
-partition: 0021	total: 82	per_label:    9	   9	   8	  10	   6	   9	   4	  11	   9	   7
-partition: 0022	total: 86	per_label:    9	   9	  10	   8	  10	   8	  13	   9	   5	   5
-partition: 0023	total: 89	per_label:   13	  10	  10	   8	   6	   9	   9	   6	   6	  12
-partition: 0024	total: 93	per_label:    7	   9	   8	  11	   3	   4	  13	  16	  10	  12
-partition: 0025	total: 97	per_label:    9	   8	  13	   8	  10	   8	  11	  10	  11	   9
-partition: 0026	total: 100	per_label:   10	  18	   8	   7	   9	  11	   6	  10	   9	  12
-partition: 0027	total: 104	per_label:   12	  11	  12	  12	  12	   4	  11	  10	  10	  10
-partition: 0028	total: 109	per_label:    7	  16	  16	  12	  12	  11	   8	   8	  11	   8
-partition: 0029	total: 113	per_label:    7	  11	  12	  14	   7	   8	   8	  13	  21	  12
-partition: 0030	total: 118	per_label:   10	  11	  14	  23	  11	  10	   7	  13	  11	   8
-partition: 0031	total: 122	per_label:   10	  14	  14	   9	  13	  12	  13	  10	  15	  12
-partition: 0032	total: 127	per_label:   10	  15	  15	   8	  16	  18	  11	   7	  14	  13
-partition: 0033	total: 132	per_label:   15	  12	  15	  12	  16	  10	  11	  13	  15	  13
-partition: 0034	total: 138	per_label:   14	  15	  21	  14	  11	  10	  14	  17	  14	   8
-partition: 0035	total: 143	per_label:   12	  15	  10	   9	  16	  15	  16	  16	  21	  13
-partition: 0036	total: 149	per_label:   19	  15	  23	  18	  13	  14	  10	  11	  11	  15
-partition: 0037	total: 155	per_label:   20	   9	  22	  18	  11	  14	  19	  16	  12	  14
-partition: 0038	total: 161	per_label:   28	   8	  18	  13	   8	  19	  16	  25	  10	  16
-partition: 0039	total: 168	per_label:   15	  10	  19	  13	  21	  22	  25	  14	  12	  17
-partition: 0040	total: 174	per_label:   15	  18	  21	  17	  20	  18	  14	  12	  22	  17
-partition: 0041	total: 181	per_label:   18	  18	  19	  18	  12	  21	  25	  19	  11	  20
-partition: 0042	total: 189	per_label:   19	  17	  24	  23	  13	  20	  20	  15	  14	  24
-partition: 0043	total: 196	per_label:   24	  19	  20	  18	  15	  15	  16	  31	  15	  23
-partition: 0044	total: 204	per_label:   25	  13	  15	  28	  16	  22	  11	  26	  19	  29
-partition: 0045	total: 212	per_label:   20	  25	  19	  21	  17	  18	  27	  18	  29	  18
-partition: 0046	total: 221	per_label:   17	  26	  30	  21	  21	  27	  19	  25	  21	  14
-partition: 0047	total: 229	per_label:   22	  21	  16	  18	  26	  15	  21	  26	  29	  35
-partition: 0048	total: 239	per_label:   25	  23	  21	  23	  26	  21	  22	  29	  27	  22
-partition: 0049	total: 248	per_label:   15	  21	  31	  28	  22	  19	  16	  35	  32	  29
-partition: 0050	total: 258	per_label:   25	  29	  21	  20	  23	  29	  22	  28	  28	  33
-partition: 0051	total: 269	per_label:   32	  20	  28	  31	  24	  24	  36	  25	  27	  22
-partition: 0052	total: 279	per_label:   25	  30	  21	  18	  28	  29	  32	  36	  37	  23
-partition: 0053	total: 290	per_label:   29	  23	  31	  30	  39	  36	  37	  12	  28	  25
-partition: 0054	total: 302	per_label:   36	  32	  27	  28	  31	  31	  26	  29	  31	  31
-partition: 0055	total: 314	per_label:   47	  28	  23	  25	  34	  37	  30	  23	  27	  40
-partition: 0056	total: 327	per_label:   33	  26	  30	  32	  29	  33	  38	  29	  40	  37
-partition: 0057	total: 340	per_label:   41	  27	  42	  43	  30	  33	  39	  25	  29	  31
-partition: 0058	total: 354	per_label:   40	  29	  41	  31	  38	  40	  37	  32	  32	  34
-partition: 0059	total: 368	per_label:   38	  39	  45	  48	  46	  27	  38	  21	  30	  36
-partition: 0060	total: 382	per_label:   31	  44	  34	  34	  46	  42	  30	  36	  41	  44
-partition: 0061	total: 398	per_label:   34	  34	  39	  37	  49	  30	  49	  45	  38	  43
-partition: 0062	total: 414	per_label:   46	  41	  32	  40	  41	  42	  48	  39	  46	  39
-partition: 0063	total: 430	per_label:   46	  57	  33	  44	  48	  36	  47	  33	  31	  55
-partition: 0064	total: 447	per_label:   48	  41	  53	  37	  51	  49	  49	  32	  47	  40
-partition: 0065	total: 465	per_label:   54	  46	  52	  30	  43	  56	  42	  40	  47	  55
-partition: 0066	total: 484	per_label:   51	  55	  47	  47	  53	  45	  49	  52	  42	  43
-partition: 0067	total: 503	per_label:   50	  48	  60	  49	  50	  49	  55	  44	  42	  56
-partition: 0068	total: 524	per_label:   43	  54	  57	  49	  49	  47	  57	  65	  50	  53
-partition: 0069	total: 545	per_label:   56	  56	  60	  49	  54	  45	  62	  56	  61	  46
-partition: 0070	total: 566	per_label:   66	  67	  56	  52	  56	  62	  55	  49	  49	  54
-partition: 0071	total: 589	per_label:   61	  58	  60	  60	  61	  62	  56	  48	  59	  64
-partition: 0072	total: 613	per_label:   65	  46	  52	  67	  71	  53	  75	  58	  66	  60
-partition: 0073	total: 637	per_label:   69	  61	  66	  72	  56	  72	  65	  50	  67	  59
-partition: 0074	total: 663	per_label:   59	  82	  61	  66	  59	  65	  50	  69	  67	  85
-partition: 0075	total: 689	per_label:   65	  81	  54	  80	  58	  73	  78	  74	  58	  68
-partition: 0076	total: 717	per_label:   76	  88	  55	  70	  89	  82	  60	  71	  68	  58
-partition: 0077	total: 745	per_label:   68	  80	  61	  81	  77	  75	  87	  84	  70	  62
-partition: 0078	total: 775	per_label:   77	  81	  75	  70	  71	  81	  72	  78	  92	  78
-partition: 0079	total: 806	per_label:   69	  98	  69	  72	  91	  79	  75	  74	  98	  81
-partition: 0080	total: 839	per_label:   92	  89	  93	  89	  69	  85	  81	  72	  82	  87
-partition: 0081	total: 872	per_label:   81	  79	  89	  90	  87	  81	 109	  92	  80	  84
-partition: 0082	total: 907	per_label:   77	  85	  73	  98	 102	 102	 101	 101	  70	  98
-partition: 0083	total: 943	per_label:   98	 102	  87	  88	  89	  93	 100	  95	  96	  95
-partition: 0084	total: 981	per_label:  102	 107	  92	  96	  98	  98	  82	  96	 108	 102
-partition: 0085	total: 1020	per_label:   95	  95	 116	 100	 102	 112	  98	 105	 104	  93
-partition: 0086	total: 1061	per_label:   89	  92	 117	 110	 114	  99	 110	 123	 109	  98
-partition: 0087	total: 1104	per_label:  125	 108	 121	 104	 106	 122	  97	 110	  92	 119
-partition: 0088	total: 1148	per_label:  121	 106	 122	 100	 114	 117	 110	 136	 105	 117
-partition: 0089	total: 1194	per_label:  131	 111	 130	 103	 132	 121	  97	 122	 134	 113
-partition: 0090	total: 1242	per_label:  119	 142	 113	 118	 128	 138	 123	 123	 121	 117
-partition: 0091	total: 1291	per_label:  139	 125	 128	 108	 141	 140	 124	 115	 148	 123
-partition: 0092	total: 1343	per_label:  142	 121	 137	 147	 132	 102	 148	 145	 139	 130
-partition: 0093	total: 1397	per_label:  120	 155	 145	 146	 133	 148	 147	 113	 140	 150
-partition: 0094	total: 1452	per_label:  133	 145	 140	 154	 149	 137	 151	 166	 134	 143
-partition: 0095	total: 1511	per_label:  157	 136	 154	 158	 138	 152	 144	 165	 155	 152
-partition: 0096	total: 1571	per_label:  151	 155	 152	 160	 167	 165	 146	 167	 162	 146
-partition: 0097	total: 1634	per_label:  154	 167	 165	 178	 158	 144	 150	 186	 171	 161
-partition: 0098	total: 1699	per_label:  163	 155	 175	 178	 148	 168	 194	 173	 173	 172
-partition: 0099	total: 1767	per_label:  173	 183	 176	 199	 172	 179	 173	 162	 177	 173
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_045.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_045.txt
deleted file mode 100644
index d50f5e5a4..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-b1_045.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-b1_045
-number_of_examples_per_label_per_shard
-partition: 0000	total: 25	per_label:    4	   3	   3	   1	   9	   1	   0	   1	   1	   2
-partition: 0001	total: 26	per_label:    3	   3	   1	   2	   2	   3	   2	   5	   3	   2
-partition: 0002	total: 27	per_label:    5	   2	   5	   4	   1	   3	   3	   2	   0	   2
-partition: 0003	total: 28	per_label:    2	   5	   3	   4	   0	   5	   2	   2	   1	   4
-partition: 0004	total: 29	per_label:    2	   3	   3	   3	   5	   5	   2	   3	   2	   1
-partition: 0005	total: 31	per_label:    1	   0	   2	   8	   2	   2	   6	   2	   4	   4
-partition: 0006	total: 32	per_label:    3	   3	   4	   5	   2	   7	   2	   1	   4	   1
-partition: 0007	total: 34	per_label:    4	   2	   2	   2	   4	   4	   4	   4	   3	   5
-partition: 0008	total: 35	per_label:    6	   3	   1	   2	   3	   1	   2	   7	   6	   4
-partition: 0009	total: 37	per_label:    3	   4	   3	   2	   3	   5	   6	   1	   4	   6
-partition: 0010	total: 39	per_label:    2	   2	   2	   6	   6	   4	   2	   3	   7	   5
-partition: 0011	total: 40	per_label:    4	   4	   4	   6	   4	   3	   5	   4	   1	   5
-partition: 0012	total: 42	per_label:    4	   5	   2	   5	   3	  10	   4	   5	   1	   3
-partition: 0013	total: 44	per_label:    7	   5	   3	   4	   6	   4	   5	   4	   2	   4
-partition: 0014	total: 46	per_label:    7	   5	   2	   4	   3	   7	   6	   4	   2	   6
-partition: 0015	total: 48	per_label:    4	   4	   3	   4	   9	   5	   3	   2	   7	   7
-partition: 0016	total: 50	per_label:    4	   3	   4	   8	   5	   8	   4	   8	   3	   3
-partition: 0017	total: 53	per_label:    4	   5	   7	   6	  11	   1	   6	   4	   5	   4
-partition: 0018	total: 55	per_label:    8	   6	   9	   5	   3	   5	   6	   3	   3	   7
-partition: 0019	total: 58	per_label:    5	   6	   5	   7	   7	   3	   5	   5	   7	   8
-partition: 0020	total: 60	per_label:    3	   6	   5	   6	   8	   4	   1	  13	   5	   9
-partition: 0021	total: 63	per_label:    6	   6	  10	   5	   6	   7	   7	   0	  11	   5
-partition: 0022	total: 66	per_label:    7	  12	   6	   6	   8	   0	   5	   9	   8	   5
-partition: 0023	total: 69	per_label:    8	  11	   7	  11	   5	   8	   4	   5	   4	   6
-partition: 0024	total: 72	per_label:   13	   5	   5	   5	   4	  12	   8	   4	   7	   9
-partition: 0025	total: 75	per_label:    6	   7	  10	   5	  10	   7	   4	  10	   8	   8
-partition: 0026	total: 79	per_label:    9	   4	   9	   8	   7	  11	   5	   8	   8	  10
-partition: 0027	total: 82	per_label:   10	  11	   6	   8	   9	   4	  15	   7	   5	   7
-partition: 0028	total: 86	per_label:   12	  10	   9	   8	   5	   9	   9	   6	   8	  10
-partition: 0029	total: 90	per_label:    5	   7	  10	  10	   7	   4	  10	  18	   9	  10
-partition: 0030	total: 94	per_label:   10	  12	  11	   8	   5	   8	  10	  10	   9	  11
-partition: 0031	total: 98	per_label:    9	  15	  10	   6	   9	  11	   8	  10	  10	  10
-partition: 0032	total: 102	per_label:   13	  11	  11	  13	  16	   3	  10	   7	   9	   9
-partition: 0033	total: 107	per_label:    5	  15	  15	  14	   8	  11	   9	   9	  12	   9
-partition: 0034	total: 112	per_label:    8	  13	  12	  12	   7	   9	   6	  13	  19	  13
-partition: 0035	total: 117	per_label:    9	  11	  14	  23	  12	   9	   8	  14	  11	   6
-partition: 0036	total: 122	per_label:   11	  14	  14	   8	  12	  14	  12	   8	  16	  13
-partition: 0037	total: 128	per_label:   11	  14	  15	   9	  18	  18	  11	   7	  13	  12
-partition: 0038	total: 133	per_label:   15	  12	  14	  12	  15	  10	  12	  14	  16	  13
-partition: 0039	total: 140	per_label:   14	  16	  21	  13	  10	   8	  14	  16	  19	   9
-partition: 0040	total: 146	per_label:   10	  13	  12	  12	  17	  15	  17	  17	  18	  15
-partition: 0041	total: 152	per_label:   21	  16	  25	  18	  13	  14	  10	  12	   9	  14
-partition: 0042	total: 159	per_label:   25	   8	  20	  17	  11	  15	  20	  18	  11	  14
-partition: 0043	total: 166	per_label:   22	   9	  20	  12	  11	  23	  20	  21	  13	  15
-partition: 0044	total: 174	per_label:   15	  11	  23	  16	  18	  21	  21	  17	  13	  19
-partition: 0045	total: 182	per_label:   21	  21	  17	  16	  24	  19	  16	  10	  18	  20
-partition: 0046	total: 190	per_label:   15	  17	  25	  20	  12	  19	  25	  23	  15	  19
-partition: 0047	total: 199	per_label:   26	  20	  22	  21	  13	  21	  18	  20	  13	  25
-partition: 0048	total: 208	per_label:   20	  19	  17	  25	  17	  18	  18	  28	  19	  27
-partition: 0049	total: 217	per_label:   26	  16	  20	  29	  17	  19	  15	  30	  21	  24
-partition: 0050	total: 227	per_label:   17	  29	  19	  17	  20	  30	  28	  18	  28	  21
-partition: 0051	total: 237	per_label:   19	  27	  27	  23	  28	  17	  19	  24	  32	  21
-partition: 0052	total: 248	per_label:   25	  18	  16	  22	  25	  22	  20	  39	  30	  31
-partition: 0053	total: 259	per_label:   27	  26	  34	  30	  23	  20	  21	  25	  26	  27
-partition: 0054	total: 270	per_label:   18	  29	  25	  23	  24	  30	  23	  31	  36	  31
-partition: 0055	total: 283	per_label:   35	  21	  28	  30	  26	  26	  32	  32	  22	  31
-partition: 0056	total: 295	per_label:   29	  31	  21	  21	  27	  27	  40	  36	  38	  25
-partition: 0057	total: 309	per_label:   27	  27	  35	  29	  40	  39	  38	  17	  31	  26
-partition: 0058	total: 323	per_label:   40	  31	  29	  30	  35	  34	  25	  28	  37	  34
-partition: 0059	total: 337	per_label:   50	  29	  24	  30	  36	  37	  34	  26	  31	  40
-partition: 0060	total: 352	per_label:   33	  32	  35	  34	  32	  35	  42	  30	  39	  40
-partition: 0061	total: 368	per_label:   42	  27	  43	  44	  33	  37	  43	  32	  33	  34
-partition: 0062	total: 385	per_label:   44	  32	  49	  36	  39	  40	  40	  32	  34	  39
-partition: 0063	total: 402	per_label:   44	  44	  42	  44	  56	  33	  38	  26	  34	  41
-partition: 0064	total: 420	per_label:   32	  41	  37	  45	  46	  43	  36	  45	  44	  51
-partition: 0065	total: 439	per_label:   46	  43	  43	  37	  57	  40	  53	  38	  45	  37
-partition: 0066	total: 459	per_label:   46	  58	  31	  52	  42	  33	  54	  44	  40	  59
-partition: 0067	total: 480	per_label:   54	  46	  56	  37	  61	  58	  52	  29	  44	  43
-partition: 0068	total: 501	per_label:   55	  49	  55	  40	  41	  57	  51	  45	  57	  51
-partition: 0069	total: 524	per_label:   56	  56	  53	  44	  58	  47	  52	  57	  41	  60
-partition: 0070	total: 547	per_label:   55	  59	  62	  54	  52	  58	  57	  47	  47	  56
-partition: 0071	total: 572	per_label:   49	  58	  63	  54	  56	  48	  61	  69	  57	  57
-partition: 0072	total: 598	per_label:   60	  66	  62	  51	  62	  49	  69	  63	  61	  55
-partition: 0073	total: 625	per_label:   70	  63	  66	  61	  63	  67	  56	  54	  62	  63
-partition: 0074	total: 653	per_label:   73	  63	  63	  63	  70	  72	  65	  56	  69	  59
-partition: 0075	total: 682	per_label:   75	  55	  56	  86	  64	  64	  91	  59	  60	  72
-partition: 0076	total: 713	per_label:   73	  76	  78	  69	  72	  78	  52	  66	  77	  72
-partition: 0077	total: 745	per_label:   67	 107	  62	  82	  56	  72	  71	  72	  73	  83
-partition: 0078	total: 779	per_label:   75	  81	  60	  79	  91	  90	  78	  86	  68	  71
-partition: 0079	total: 814	per_label:   81	  91	  65	  95	  89	  76	  85	  89	  79	  64
-partition: 0080	total: 850	per_label:   81	  87	  82	  73	  78	  98	  83	  85	  94	  89
-partition: 0081	total: 889	per_label:   77	 107	  76	  80	  99	  84	  86	  83	 110	  87
-partition: 0082	total: 929	per_label:   97	  96	 106	  95	  81	  96	  93	  78	  87	 100
-partition: 0083	total: 971	per_label:   92	  89	  88	 110	 100	  91	 119	 100	  86	  96
-partition: 0084	total: 1014	per_label:   97	  96	  84	 103	 102	 109	 115	 121	  87	 100
-partition: 0085	total: 1060	per_label:  102	 116	 107	  89	 106	 107	 103	  96	 113	 121
-partition: 0086	total: 1108	per_label:  112	 114	 116	 123	 116	 118	  86	 120	 110	  93
-partition: 0087	total: 1158	per_label:   98	 108	 124	 108	 114	 122	 129	 118	 124	 113
-partition: 0088	total: 1210	per_label:  131	 114	 126	 115	 121	 127	 105	 133	 111	 127
-partition: 0089	total: 1264	per_label:  135	 116	 140	 119	 124	 125	 121	 149	 114	 121
-partition: 0090	total: 1321	per_label:  143	 121	 144	 112	 146	 134	 110	 133	 145	 133
-partition: 0091	total: 1381	per_label:  130	 157	 125	 135	 144	 153	 137	 129	 141	 130
-partition: 0092	total: 1443	per_label:  157	 136	 144	 128	 150	 145	 144	 136	 160	 143
-partition: 0093	total: 1508	per_label:  158	 146	 157	 149	 144	 136	 164	 155	 154	 145
-partition: 0094	total: 1575	per_label:  136	 169	 162	 173	 166	 151	 163	 137	 151	 167
-partition: 0095	total: 1646	per_label:  154	 154	 162	 180	 156	 150	 171	 194	 166	 159
-partition: 0096	total: 1720	per_label:  170	 163	 171	 163	 191	 182	 158	 194	 165	 163
-partition: 0097	total: 1798	per_label:  169	 181	 178	 206	 161	 175	 160	 189	 195	 184
-partition: 0098	total: 1879	per_label:  185	 178	 192	 191	 170	 183	 205	 197	 195	 183
-partition: 0099	total: 1963	per_label:  192	 202	 194	 222	 185	 198	 199	 184	 194	 193
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-iid-balanced.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-iid-balanced.txt
deleted file mode 100644
index f1f5beffc..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-iid-balanced.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-iid-balanced
-number_of_examples_per_label_per_shard
-partition: 0000	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0001	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0002	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0003	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0004	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0005	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0006	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0007	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0008	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0009	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0010	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0011	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0012	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0013	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0014	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0015	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0016	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0017	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0018	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0019	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0020	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0021	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0022	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0023	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0024	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0025	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0026	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0027	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0028	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0029	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0030	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0031	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0032	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0033	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0034	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0035	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0036	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0037	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0038	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0039	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0040	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0041	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0042	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0043	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0044	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0045	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0046	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0047	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0048	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0049	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0050	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0051	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0052	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0053	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0054	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0055	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0056	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0057	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0058	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0059	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0060	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0061	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0062	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0063	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0064	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0065	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0066	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0067	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0068	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0069	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0070	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0071	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0072	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0073	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0074	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0075	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0076	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0077	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0078	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0079	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0080	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0081	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0082	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0083	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0084	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0085	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0086	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0087	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0088	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0089	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0090	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0091	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0092	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0093	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0094	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0095	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0096	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0097	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0098	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: 0099	total: 450	per_label:   45	  45	  45	  45	  45	  45	  45	  45	  45	  45
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-01cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-01cpp.txt
deleted file mode 100644
index 71d577f92..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-01cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-01cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0001	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0002	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0003	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0004	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0005	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0006	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0007	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0008	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0009	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0010	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0011	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0012	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0013	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0014	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0015	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0017	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0018	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0019	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0020	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0021	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0022	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0023	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0024	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0025	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0026	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0027	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0028	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0029	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0030	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0031	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0032	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0033	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0034	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0035	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0036	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0037	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0038	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0039	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0040	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0041	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0042	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0043	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0044	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0045	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0046	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0048	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0049	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0050	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0051	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0052	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0053	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0054	total: 450	per_label:    0	   0	 450	   0	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0056	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0057	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0058	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0059	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0060	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0061	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0062	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0063	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0064	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0065	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0066	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0067	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0068	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0069	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0070	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0071	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0072	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0073	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0074	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0075	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0076	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0077	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0078	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0079	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 450
-partition: 0080	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0081	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0082	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0083	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0084	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0085	total: 450	per_label:  450	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0086	total: 450	per_label:    0	 450	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0087	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0088	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: 0089	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0090	total: 450	per_label:    0	   0	   0	   0	   0	 450	   0	   0	   0	   0
-partition: 0091	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0092	total: 450	per_label:    0	   0	   0	   0	 450	   0	   0	   0	   0	   0
-partition: 0093	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0094	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0095	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0096	total: 450	per_label:    0	   0	   0	   0	   0	   0	 450	   0	   0	   0
-partition: 0097	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 450	   0	   0
-partition: 0098	total: 450	per_label:    0	   0	   0	 450	   0	   0	   0	   0	   0	   0
-partition: 0099	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 450	   0
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-02cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-02cpp.txt
deleted file mode 100644
index 52edd723c..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-02cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-02cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	   0	 225
-partition: 0001	total: 450	per_label:    0	 225	   0	   0	   0	   0	 225	   0	   0	   0
-partition: 0002	total: 450	per_label:    0	   0	 225	   0	   0	   0	 225	   0	   0	   0
-partition: 0003	total: 450	per_label:  225	   0	   0	   0	   0	   0	 225	   0	   0	   0
-partition: 0004	total: 450	per_label:    0	   0	   0	 225	   0	 225	   0	   0	   0	   0
-partition: 0005	total: 450	per_label:    0	 225	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0006	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	   0	 225
-partition: 0007	total: 450	per_label:    0	   0	 225	   0	   0	 225	   0	   0	   0	   0
-partition: 0008	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0009	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	   0	 225	   0
-partition: 0010	total: 450	per_label:    0	   0	 225	   0	   0	 225	   0	   0	   0	   0
-partition: 0011	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 225	   0	 225
-partition: 0012	total: 450	per_label:    0	   0	   0	   0	 225	   0	 225	   0	   0	   0
-partition: 0013	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	 225	   0	   0
-partition: 0014	total: 450	per_label:    0	   0	 225	   0	 225	   0	   0	   0	   0	   0
-partition: 0015	total: 450	per_label:  225	   0	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	   0	 225	   0
-partition: 0017	total: 450	per_label:  225	 225	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0018	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0019	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	 225	   0
-partition: 0020	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	   0	 225	   0
-partition: 0021	total: 450	per_label:    0	 225	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0022	total: 450	per_label:    0	   0	   0	   0	   0	 225	 225	   0	   0	   0
-partition: 0023	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	 225	   0	   0
-partition: 0024	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	   0	 225	   0
-partition: 0025	total: 450	per_label:    0	   0	 225	   0	   0	 225	   0	   0	   0	   0
-partition: 0026	total: 450	per_label:    0	 225	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0027	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	 225	   0	   0
-partition: 0028	total: 450	per_label:    0	   0	 225	   0	   0	 225	   0	   0	   0	   0
-partition: 0029	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	 225	   0	   0
-partition: 0030	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	   0	 225	   0
-partition: 0031	total: 450	per_label:  225	   0	 225	   0	   0	   0	   0	   0	   0	   0
-partition: 0032	total: 450	per_label:    0	   0	 225	   0	   0	 225	   0	   0	   0	   0
-partition: 0033	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	   0	 225	   0
-partition: 0034	total: 450	per_label:    0	   0	   0	 225	   0	   0	 225	   0	   0	   0
-partition: 0035	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	 225	   0	   0
-partition: 0036	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	 225	   0	   0
-partition: 0037	total: 450	per_label:    0	   0	 225	 225	   0	   0	   0	   0	   0	   0
-partition: 0038	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	 225	   0	   0
-partition: 0039	total: 450	per_label:  225	   0	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0040	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	   0	   0	 225
-partition: 0041	total: 450	per_label:    0	   0	 225	   0	   0	   0	   0	   0	   0	 225
-partition: 0042	total: 450	per_label:    0	 225	 225	   0	   0	   0	   0	   0	   0	   0
-partition: 0043	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	   0	   0	 225
-partition: 0044	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	   0	   0	 225
-partition: 0045	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	   0	 225
-partition: 0046	total: 450	per_label:  225	   0	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0048	total: 450	per_label:  225	   0	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0049	total: 450	per_label:  225	   0	   0	   0	   0	   0	 225	   0	   0	   0
-partition: 0050	total: 450	per_label:    0	   0	   0	 225	   0	 225	   0	   0	   0	   0
-partition: 0051	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	 225	   0
-partition: 0052	total: 450	per_label:    0	 225	   0	   0	   0	 225	   0	   0	   0	   0
-partition: 0053	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	   0	 225	   0
-partition: 0054	total: 450	per_label:    0	 225	 225	   0	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 450	per_label:    0	 225	   0	   0	   0	 225	   0	   0	   0	   0
-partition: 0056	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 225	 225
-partition: 0057	total: 450	per_label:    0	 225	 225	   0	   0	   0	   0	   0	   0	   0
-partition: 0058	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	   0	 225	   0
-partition: 0059	total: 450	per_label:  225	   0	   0	   0	   0	   0	 225	   0	   0	   0
-partition: 0060	total: 450	per_label:    0	 225	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0061	total: 450	per_label:  225	   0	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0062	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	   0	 225
-partition: 0063	total: 450	per_label:    0	 225	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0064	total: 450	per_label:  225	   0	   0	   0	 225	   0	   0	   0	   0	   0
-partition: 0065	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	 225	   0	   0
-partition: 0066	total: 450	per_label:  225	   0	 225	   0	   0	   0	   0	   0	   0	   0
-partition: 0067	total: 450	per_label:    0	   0	 225	 225	   0	   0	   0	   0	   0	   0
-partition: 0068	total: 450	per_label:    0	 225	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0069	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 225	 225
-partition: 0070	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	   0	 225
-partition: 0071	total: 450	per_label:    0	 225	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0072	total: 450	per_label:    0	   0	   0	   0	 225	 225	   0	   0	   0	   0
-partition: 0073	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	   0	 225
-partition: 0074	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0075	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	   0	   0	 225
-partition: 0076	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	 225	   0
-partition: 0077	total: 450	per_label:  225	   0	   0	   0	   0	   0	   0	   0	   0	 225
-partition: 0078	total: 450	per_label:  225	   0	   0	 225	   0	   0	   0	   0	   0	   0
-partition: 0079	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	 225	   0
-partition: 0080	total: 450	per_label:  225	 225	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0081	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	 225	   0	   0
-partition: 0082	total: 450	per_label:    0	   0	   0	   0	   0	 225	 225	   0	   0	   0
-partition: 0083	total: 450	per_label:    0	   0	   0	   0	 225	   0	   0	   0	 225	   0
-partition: 0084	total: 450	per_label:    0	   0	   0	 225	 225	   0	   0	   0	   0	   0
-partition: 0085	total: 450	per_label:    0	   0	   0	 225	   0	 225	   0	   0	   0	   0
-partition: 0086	total: 450	per_label:    0	   0	   0	 225	 225	   0	   0	   0	   0	   0
-partition: 0087	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	   0	 225	   0
-partition: 0088	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	 225	   0	   0
-partition: 0089	total: 450	per_label:    0	   0	   0	   0	   0	 225	   0	   0	 225	   0
-partition: 0090	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	   0	 225	   0
-partition: 0091	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	 225	   0	   0
-partition: 0092	total: 450	per_label:    0	   0	   0	 225	   0	   0	   0	 225	   0	   0
-partition: 0093	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 225	 225	   0
-partition: 0094	total: 450	per_label:    0	   0	   0	   0	   0	   0	   0	 225	 225	   0
-partition: 0095	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	 225	   0	   0
-partition: 0096	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	 225	   0	   0
-partition: 0097	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	 225	   0	   0
-partition: 0098	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	 225	   0	   0
-partition: 0099	total: 450	per_label:    0	   0	   0	   0	   0	   0	 225	 225	   0	   0
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-03cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-03cpp.txt
deleted file mode 100644
index a0159675b..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-03cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-03cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 450	per_label:    0	   0	   0	 150	 150	   0	   0	   0	   0	 150
-partition: 0001	total: 450	per_label:    0	 150	   0	   0	   0	 150	 150	   0	   0	   0
-partition: 0002	total: 450	per_label:    0	   0	 150	   0	   0	 150	 150	   0	   0	   0
-partition: 0003	total: 450	per_label:  150	   0	   0	   0	 150	   0	 150	   0	   0	   0
-partition: 0004	total: 450	per_label:    0	   0	   0	 150	   0	 150	   0	 150	   0	   0
-partition: 0005	total: 450	per_label:    0	 150	 150	   0	 150	   0	   0	   0	   0	   0
-partition: 0006	total: 450	per_label:    0	   0	   0	   0	 150	 150	   0	   0	   0	 150
-partition: 0007	total: 450	per_label:    0	   0	 150	   0	   0	 150	   0	   0	   0	 150
-partition: 0008	total: 450	per_label:    0	 150	   0	   0	   0	   0	   0	   0	 150	 150
-partition: 0009	total: 450	per_label:    0	   0	 150	   0	   0	   0	   0	   0	 150	 150
-partition: 0010	total: 450	per_label:  150	   0	 150	   0	   0	 150	   0	   0	   0	   0
-partition: 0011	total: 450	per_label:    0	   0	   0	   0	   0	 150	   0	 150	   0	 150
-partition: 0012	total: 450	per_label:    0	   0	   0	   0	 150	 150	 150	   0	   0	   0
-partition: 0013	total: 450	per_label:    0	 150	   0	 150	   0	   0	   0	 150	   0	   0
-partition: 0014	total: 450	per_label:    0	   0	 150	   0	 150	   0	   0	 150	   0	   0
-partition: 0015	total: 450	per_label:  150	 150	   0	 150	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 450	per_label:    0	   0	 150	   0	   0	 150	   0	   0	 150	   0
-partition: 0017	total: 450	per_label:  150	 150	   0	   0	   0	   0	   0	   0	   0	 150
-partition: 0018	total: 450	per_label:    0	 150	 150	   0	   0	   0	   0	   0	   0	 150
-partition: 0019	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	   0	 150	   0
-partition: 0020	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	   0	 150	   0
-partition: 0021	total: 450	per_label:    0	 150	   0	 150	   0	 150	   0	   0	   0	   0
-partition: 0022	total: 450	per_label:    0	 150	   0	   0	   0	 150	 150	   0	   0	   0
-partition: 0023	total: 450	per_label:  150	   0	 150	   0	   0	   0	   0	 150	   0	   0
-partition: 0024	total: 450	per_label:    0	 150	   0	   0	   0	   0	 150	   0	 150	   0
-partition: 0025	total: 450	per_label:    0	   0	 150	   0	   0	 150	   0	 150	   0	   0
-partition: 0026	total: 450	per_label:    0	 150	   0	   0	 150	 150	   0	   0	   0	   0
-partition: 0027	total: 450	per_label:    0	 150	   0	 150	   0	   0	   0	 150	   0	   0
-partition: 0028	total: 450	per_label:    0	   0	 150	   0	 150	 150	   0	   0	   0	   0
-partition: 0029	total: 450	per_label:    0	   0	 150	   0	   0	 150	   0	 150	   0	   0
-partition: 0030	total: 450	per_label:    0	 150	   0	   0	   0	   0	 150	   0	 150	   0
-partition: 0031	total: 450	per_label:  150	   0	 150	   0	   0	   0	   0	   0	 150	   0
-partition: 0032	total: 450	per_label:  150	   0	 150	   0	   0	 150	   0	   0	   0	   0
-partition: 0033	total: 450	per_label:    0	   0	 150	 150	   0	   0	   0	   0	 150	   0
-partition: 0034	total: 450	per_label:    0	   0	   0	 150	   0	   0	 150	   0	 150	   0
-partition: 0035	total: 450	per_label:    0	   0	 150	 150	   0	   0	   0	 150	   0	   0
-partition: 0036	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0037	total: 450	per_label:    0	   0	 150	 150	   0	   0	   0	   0	 150	   0
-partition: 0038	total: 450	per_label:    0	   0	   0	   0	 150	   0	 150	 150	   0	   0
-partition: 0039	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0040	total: 450	per_label:    0	   0	   0	 150	   0	   0	 150	   0	   0	 150
-partition: 0041	total: 450	per_label:  150	   0	 150	   0	   0	   0	   0	   0	   0	 150
-partition: 0042	total: 450	per_label:    0	 150	 150	   0	   0	   0	   0	 150	   0	   0
-partition: 0043	total: 450	per_label:    0	   0	   0	   0	   0	 150	   0	   0	 150	 150
-partition: 0044	total: 450	per_label:    0	 150	   0	   0	   0	 150	   0	   0	   0	 150
-partition: 0045	total: 450	per_label:    0	 150	   0	   0	   0	   0	 150	   0	   0	 150
-partition: 0046	total: 450	per_label:  150	   0	 150	 150	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 450	per_label:  150	   0	   0	 150	   0	   0	   0	   0	   0	 150
-partition: 0048	total: 450	per_label:  150	   0	   0	 150	   0	   0	   0	   0	   0	 150
-partition: 0049	total: 450	per_label:  150	 150	   0	   0	   0	   0	 150	   0	   0	   0
-partition: 0050	total: 450	per_label:    0	 150	   0	 150	   0	 150	   0	   0	   0	   0
-partition: 0051	total: 450	per_label:    0	   0	   0	   0	   0	   0	 150	   0	 150	 150
-partition: 0052	total: 450	per_label:  150	 150	   0	   0	   0	 150	   0	   0	   0	   0
-partition: 0053	total: 450	per_label:  150	   0	   0	   0	   0	   0	   0	 150	 150	   0
-partition: 0054	total: 450	per_label:    0	 150	 150	 150	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 450	per_label:  150	 150	   0	   0	   0	 150	   0	   0	   0	   0
-partition: 0056	total: 450	per_label:  150	   0	   0	   0	   0	   0	   0	   0	 150	 150
-partition: 0057	total: 450	per_label:    0	 150	 150	   0	   0	   0	   0	   0	 150	   0
-partition: 0058	total: 450	per_label:    0	   0	   0	   0	   0	 150	 150	   0	 150	   0
-partition: 0059	total: 450	per_label:  150	   0	 150	   0	   0	   0	 150	   0	   0	   0
-partition: 0060	total: 450	per_label:    0	 150	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0061	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0062	total: 450	per_label:    0	 150	   0	   0	 150	   0	   0	   0	   0	 150
-partition: 0063	total: 450	per_label:    0	 150	   0	 150	 150	   0	   0	   0	   0	   0
-partition: 0064	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0065	total: 450	per_label:    0	 150	   0	   0	   0	 150	   0	 150	   0	   0
-partition: 0066	total: 450	per_label:  150	   0	 150	   0	 150	   0	   0	   0	   0	   0
-partition: 0067	total: 450	per_label:    0	   0	 150	 150	 150	   0	   0	   0	   0	   0
-partition: 0068	total: 450	per_label:    0	 150	 150	   0	   0	   0	 150	   0	   0	   0
-partition: 0069	total: 450	per_label:    0	   0	   0	   0	   0	   0	 150	 150	 150	   0
-partition: 0070	total: 450	per_label:    0	   0	   0	   0	   0	   0	 150	   0	 150	 150
-partition: 0071	total: 450	per_label:  150	   0	 150	 150	   0	   0	   0	   0	   0	   0
-partition: 0072	total: 450	per_label:    0	   0	   0	 150	   0	   0	   0	 150	   0	 150
-partition: 0073	total: 450	per_label:    0	 150	   0	   0	 150	   0	 150	   0	   0	   0
-partition: 0074	total: 450	per_label:    0	   0	   0	   0	 150	 150	   0	 150	   0	   0
-partition: 0075	total: 450	per_label:    0	   0	   0	 150	   0	   0	   0	 150	 150	   0
-partition: 0076	total: 450	per_label:  150	 150	 150	   0	   0	   0	   0	   0	   0	   0
-partition: 0077	total: 450	per_label:    0	   0	   0	   0	   0	 150	   0	   0	 150	 150
-partition: 0078	total: 450	per_label:  150	   0	   0	 150	   0	   0	   0	   0	   0	 150
-partition: 0079	total: 450	per_label:    0	 150	   0	   0	   0	   0	 150	   0	   0	 150
-partition: 0080	total: 450	per_label:  150	   0	   0	 150	   0	   0	   0	 150	   0	   0
-partition: 0081	total: 450	per_label:    0	   0	   0	   0	   0	 150	   0	   0	 150	 150
-partition: 0082	total: 450	per_label:    0	   0	 150	   0	   0	 150	   0	 150	   0	   0
-partition: 0083	total: 450	per_label:    0	   0	   0	 150	   0	   0	   0	 150	   0	 150
-partition: 0084	total: 450	per_label:  150	   0	   0	   0	 150	   0	   0	 150	   0	   0
-partition: 0085	total: 450	per_label:  150	   0	 150	   0	   0	   0	   0	   0	 150	   0
-partition: 0086	total: 450	per_label:    0	   0	   0	   0	   0	 150	 150	   0	 150	   0
-partition: 0087	total: 450	per_label:    0	   0	   0	 150	   0	   0	 150	   0	 150	   0
-partition: 0088	total: 450	per_label:    0	   0	   0	   0	   0	 150	 150	   0	   0	 150
-partition: 0089	total: 450	per_label:    0	   0	   0	 150	 150	   0	   0	   0	 150	   0
-partition: 0090	total: 450	per_label:    0	   0	   0	   0	   0	 150	   0	 150	 150	   0
-partition: 0091	total: 450	per_label:    0	   0	   0	   0	   0	   0	 150	 150	   0	 150
-partition: 0092	total: 450	per_label:    0	   0	   0	 150	 150	   0	   0	   0	   0	 150
-partition: 0093	total: 450	per_label:    0	   0	   0	   0	 150	   0	   0	   0	 150	 150
-partition: 0094	total: 450	per_label:    0	   0	   0	   0	 150	   0	 150	 150	   0	   0
-partition: 0095	total: 450	per_label:    0	   0	   0	 150	 150	   0	 150	   0	   0	   0
-partition: 0096	total: 450	per_label:    0	   0	   0	   0	 150	   0	 150	   0	   0	 150
-partition: 0097	total: 450	per_label:    0	   0	   0	 150	 150	   0	 150	   0	   0	   0
-partition: 0098	total: 450	per_label:    0	   0	   0	 150	   0	   0	 150	   0	 150	   0
-partition: 0099	total: 450	per_label:    0	   0	   0	   0	   0	   0	 150	 150	 150	   0
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-04cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-04cpp.txt
deleted file mode 100644
index 7870dbd99..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-04cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-04cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 448	per_label:    0	 112	   0	 112	 112	   0	   0	   0	   0	 112
-partition: 0001	total: 448	per_label:    0	 112	 112	   0	   0	 112	 112	   0	   0	   0
-partition: 0002	total: 448	per_label:    0	   0	 112	   0	   0	 112	 112	 112	   0	   0
-partition: 0003	total: 448	per_label:  112	   0	   0	   0	 112	   0	 112	   0	   0	 112
-partition: 0004	total: 448	per_label:    0	   0	   0	 112	   0	 112	   0	 112	   0	 112
-partition: 0005	total: 448	per_label:    0	 112	 112	   0	 112	   0	   0	 112	   0	   0
-partition: 0006	total: 448	per_label:    0	   0	   0	   0	 112	 112	   0	 112	   0	 112
-partition: 0007	total: 448	per_label:    0	   0	 112	   0	 112	 112	   0	   0	   0	 112
-partition: 0008	total: 448	per_label:    0	 112	   0	   0	   0	   0	 112	   0	 112	 112
-partition: 0009	total: 448	per_label:    0	   0	 112	   0	   0	 112	   0	   0	 112	 112
-partition: 0010	total: 448	per_label:  112	   0	 112	 112	   0	 112	   0	   0	   0	   0
-partition: 0011	total: 448	per_label:    0	   0	   0	   0	 112	 112	   0	 112	   0	 112
-partition: 0012	total: 448	per_label:    0	   0	   0	   0	 112	 112	 112	   0	   0	 112
-partition: 0013	total: 448	per_label:  112	 112	   0	 112	   0	   0	   0	 112	   0	   0
-partition: 0014	total: 448	per_label:    0	   0	 112	 112	 112	   0	   0	 112	   0	   0
-partition: 0015	total: 448	per_label:  112	 112	   0	 112	   0	   0	 112	   0	   0	   0
-partition: 0016	total: 448	per_label:    0	 112	 112	   0	   0	 112	   0	   0	 112	   0
-partition: 0017	total: 448	per_label:  112	 112	   0	   0	   0	   0	 112	   0	   0	 112
-partition: 0018	total: 448	per_label:    0	 112	 112	   0	   0	 112	   0	   0	   0	 112
-partition: 0019	total: 448	per_label:  112	   0	 112	   0	 112	   0	   0	   0	 112	   0
-partition: 0020	total: 448	per_label:  112	   0	   0	   0	 112	 112	   0	   0	 112	   0
-partition: 0021	total: 448	per_label:    0	 112	   0	 112	   0	 112	   0	   0	 112	   0
-partition: 0022	total: 448	per_label:    0	 112	   0	   0	   0	 112	 112	   0	 112	   0
-partition: 0023	total: 448	per_label:  112	   0	 112	 112	   0	   0	   0	 112	   0	   0
-partition: 0024	total: 448	per_label:    0	 112	   0	   0	   0	   0	 112	   0	 112	 112
-partition: 0025	total: 448	per_label:    0	 112	 112	   0	   0	 112	   0	 112	   0	   0
-partition: 0026	total: 448	per_label:    0	 112	   0	 112	 112	 112	   0	   0	   0	   0
-partition: 0027	total: 448	per_label:    0	 112	   0	 112	   0	   0	 112	 112	   0	   0
-partition: 0028	total: 448	per_label:    0	   0	 112	 112	 112	 112	   0	   0	   0	   0
-partition: 0029	total: 448	per_label:    0	   0	 112	   0	   0	 112	   0	 112	   0	 112
-partition: 0030	total: 448	per_label:    0	 112	 112	   0	   0	   0	   0	 112	 112	   0
-partition: 0031	total: 448	per_label:  112	   0	 112	   0	 112	   0	   0	   0	 112	   0
-partition: 0032	total: 448	per_label:  112	   0	 112	   0	   0	 112	   0	   0	 112	   0
-partition: 0033	total: 448	per_label:    0	   0	 112	 112	   0	   0	   0	 112	 112	   0
-partition: 0034	total: 448	per_label:    0	   0	   0	 112	 112	   0	 112	   0	 112	   0
-partition: 0035	total: 448	per_label:    0	   0	 112	 112	   0	 112	   0	 112	   0	   0
-partition: 0036	total: 448	per_label:  112	   0	   0	   0	 112	   0	 112	 112	   0	   0
-partition: 0037	total: 448	per_label:    0	 112	 112	 112	   0	   0	   0	   0	 112	   0
-partition: 0038	total: 448	per_label:    0	   0	   0	   0	 112	   0	 112	 112	 112	   0
-partition: 0039	total: 448	per_label:  112	   0	   0	   0	 112	 112	   0	 112	   0	   0
-partition: 0040	total: 448	per_label:  112	   0	   0	 112	   0	   0	 112	   0	   0	 112
-partition: 0041	total: 448	per_label:  112	 112	 112	   0	   0	   0	   0	   0	   0	 112
-partition: 0042	total: 448	per_label:    0	 112	 112	   0	   0	   0	   0	 112	 112	   0
-partition: 0043	total: 448	per_label:    0	   0	   0	 112	   0	 112	   0	   0	 112	 112
-partition: 0044	total: 448	per_label:    0	 112	   0	   0	   0	 112	   0	   0	 112	 112
-partition: 0045	total: 448	per_label:    0	 112	   0	   0	   0	   0	 112	   0	 112	 112
-partition: 0046	total: 448	per_label:  112	   0	 112	 112	   0	 112	   0	   0	   0	   0
-partition: 0047	total: 448	per_label:  112	   0	   0	 112	 112	   0	   0	   0	   0	 112
-partition: 0048	total: 448	per_label:  112	   0	   0	 112	   0	   0	   0	 112	   0	 112
-partition: 0049	total: 448	per_label:  112	 112	   0	   0	   0	   0	 112	   0	   0	 112
-partition: 0050	total: 448	per_label:    0	 112	   0	 112	   0	 112	   0	   0	   0	 112
-partition: 0051	total: 448	per_label:    0	   0	   0	   0	 112	   0	 112	   0	 112	 112
-partition: 0052	total: 448	per_label:  112	 112	   0	   0	 112	 112	   0	   0	   0	   0
-partition: 0053	total: 448	per_label:  112	   0	   0	   0	   0	 112	   0	 112	 112	   0
-partition: 0054	total: 448	per_label:    0	 112	 112	 112	   0	 112	   0	   0	   0	   0
-partition: 0055	total: 448	per_label:  112	 112	   0	   0	   0	 112	 112	   0	   0	   0
-partition: 0056	total: 448	per_label:  112	   0	   0	   0	   0	   0	 112	   0	 112	 112
-partition: 0057	total: 448	per_label:    0	 112	 112	   0	   0	   0	   0	   0	 112	 112
-partition: 0058	total: 448	per_label:    0	   0	   0	   0	 112	 112	 112	   0	 112	   0
-partition: 0059	total: 448	per_label:  112	 112	 112	   0	   0	   0	 112	   0	   0	   0
-partition: 0060	total: 448	per_label:    0	 112	 112	   0	 112	   0	   0	 112	   0	   0
-partition: 0061	total: 448	per_label:  112	 112	   0	   0	 112	   0	   0	 112	   0	   0
-partition: 0062	total: 448	per_label:    0	 112	 112	   0	 112	   0	   0	   0	   0	 112
-partition: 0063	total: 448	per_label:    0	 112	   0	 112	 112	   0	   0	 112	   0	   0
-partition: 0064	total: 448	per_label:  112	   0	   0	   0	 112	   0	 112	 112	   0	   0
-partition: 0065	total: 448	per_label:    0	 112	   0	   0	 112	 112	   0	 112	   0	   0
-partition: 0066	total: 448	per_label:  112	   0	 112	 112	 112	   0	   0	   0	   0	   0
-partition: 0067	total: 448	per_label:    0	 112	 112	 112	 112	   0	   0	   0	   0	   0
-partition: 0068	total: 448	per_label:    0	 112	 112	   0	   0	   0	 112	   0	 112	   0
-partition: 0069	total: 448	per_label:    0	 112	   0	   0	   0	   0	 112	 112	 112	   0
-partition: 0070	total: 448	per_label:    0	   0	   0	   0	   0	 112	 112	   0	 112	 112
-partition: 0071	total: 448	per_label:  112	   0	 112	 112	 112	   0	   0	   0	   0	   0
-partition: 0072	total: 448	per_label:    0	   0	   0	 112	   0	 112	   0	 112	   0	 112
-partition: 0073	total: 448	per_label:    0	 112	   0	   0	 112	   0	 112	   0	 112	   0
-partition: 0074	total: 448	per_label:    0	   0	   0	   0	 112	 112	 112	 112	   0	   0
-partition: 0075	total: 448	per_label:    0	   0	   0	 112	   0	 112	   0	 112	 112	   0
-partition: 0076	total: 448	per_label:  112	 112	 112	 112	   0	   0	   0	   0	   0	   0
-partition: 0077	total: 448	per_label:    0	   0	   0	   0	   0	 112	 112	   0	 112	 112
-partition: 0078	total: 448	per_label:  112	   0	   0	 112	   0	   0	   0	 112	   0	 112
-partition: 0079	total: 448	per_label:    0	 112	 112	   0	   0	   0	 112	   0	   0	 112
-partition: 0080	total: 448	per_label:    0	   0	   0	   0	 112	   0	 112	 112	   0	 112
-partition: 0081	total: 448	per_label:    0	 112	   0	 112	 112	   0	   0	   0	 112	   0
-partition: 0082	total: 448	per_label:  112	   0	 112	 112	   0	   0	   0	 112	   0	   0
-partition: 0083	total: 448	per_label:  112	   0	 112	   0	 112	   0	   0	   0	 112	   0
-partition: 0084	total: 448	per_label:  112	   0	 112	   0	 112	   0	   0	   0	 112	   0
-partition: 0085	total: 448	per_label:    0	   0	   0	 112	 112	   0	   0	   0	 112	 112
-partition: 0086	total: 448	per_label:    0	   0	   0	 112	 112	 112	   0	   0	   0	 112
-partition: 0087	total: 448	per_label:  112	   0	   0	   0	   0	 112	 112	 112	   0	   0
-partition: 0088	total: 448	per_label:    0	   0	   0	   0	 112	 112	   0	   0	 112	 112
-partition: 0089	total: 448	per_label:    0	   0	   0	   0	 112	 112	 112	   0	   0	 112
-partition: 0090	total: 448	per_label:    0	   0	 112	   0	   0	   0	 112	 112	 112	   0
-partition: 0091	total: 448	per_label:    0	   0	 112	 112	   0	   0	 112	 112	   0	   0
-partition: 0092	total: 448	per_label:  112	   0	 112	   0	   0	   0	   0	 112	 112	   0
-partition: 0093	total: 448	per_label:  112	   0	   0	   0	   0	   0	 112	 112	   0	 112
-partition: 0094	total: 448	per_label:  112	   0	   0	 112	   0	   0	 112	   0	   0	 112
-partition: 0095	total: 448	per_label:    0	   0	   0	 112	   0	   0	 112	 112	   0	 112
-partition: 0096	total: 448	per_label:  112	   0	   0	 112	   0	   0	 112	 112	   0	   0
-partition: 0097	total: 448	per_label:  112	   0	   0	 112	   0	   0	 112	   0	 112	   0
-partition: 0098	total: 448	per_label:  112	   0	   0	 112	   0	   0	   0	 112	 112	   0
-partition: 0099	total: 448	per_label:  112	   0	   0	   0	   0	   0	 112	   0	 112	 112
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-05cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-05cpp.txt
deleted file mode 100644
index d21035b96..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-05cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-05cpp
-number_of_examples_per_label_per_shard
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-partition: 0001	total: 450	per_label:    0	  90	  90	  90	   0	  90	  90	   0	   0	   0
-partition: 0002	total: 450	per_label:    0	  90	  90	   0	   0	  90	  90	  90	   0	   0
-partition: 0003	total: 450	per_label:   90	   0	   0	   0	  90	  90	  90	   0	   0	  90
-partition: 0004	total: 450	per_label:    0	   0	  90	  90	   0	  90	   0	  90	   0	  90
-partition: 0005	total: 450	per_label:    0	  90	  90	   0	  90	   0	  90	  90	   0	   0
-partition: 0006	total: 450	per_label:    0	   0	   0	   0	  90	  90	   0	  90	  90	  90
-partition: 0007	total: 450	per_label:    0	   0	  90	   0	  90	  90	   0	  90	   0	  90
-partition: 0008	total: 450	per_label:    0	  90	   0	   0	  90	   0	  90	   0	  90	  90
-partition: 0009	total: 450	per_label:    0	  90	  90	   0	   0	  90	   0	   0	  90	  90
-partition: 0010	total: 450	per_label:   90	   0	  90	  90	   0	  90	   0	   0	  90	   0
-partition: 0011	total: 450	per_label:   90	   0	   0	   0	  90	  90	   0	  90	   0	  90
-partition: 0012	total: 450	per_label:    0	   0	   0	   0	  90	  90	  90	   0	  90	  90
-partition: 0013	total: 450	per_label:   90	  90	   0	  90	   0	   0	  90	  90	   0	   0
-partition: 0014	total: 450	per_label:    0	   0	  90	  90	  90	   0	   0	  90	  90	   0
-partition: 0015	total: 450	per_label:   90	  90	   0	  90	   0	   0	  90	   0	  90	   0
-partition: 0016	total: 450	per_label:    0	  90	  90	   0	   0	  90	  90	   0	  90	   0
-partition: 0017	total: 450	per_label:   90	  90	   0	   0	   0	   0	  90	   0	  90	  90
-partition: 0018	total: 450	per_label:    0	  90	  90	   0	   0	  90	   0	   0	  90	  90
-partition: 0019	total: 450	per_label:   90	   0	  90	   0	  90	  90	   0	   0	  90	   0
-partition: 0020	total: 450	per_label:   90	   0	   0	   0	  90	  90	   0	  90	  90	   0
-partition: 0021	total: 450	per_label:    0	  90	   0	  90	  90	  90	   0	   0	  90	   0
-partition: 0022	total: 450	per_label:    0	  90	   0	  90	   0	  90	  90	   0	  90	   0
-partition: 0023	total: 450	per_label:   90	  90	  90	  90	   0	   0	   0	  90	   0	   0
-partition: 0024	total: 450	per_label:    0	  90	  90	   0	   0	   0	  90	   0	  90	  90
-partition: 0025	total: 450	per_label:    0	  90	  90	   0	   0	  90	   0	  90	  90	   0
-partition: 0026	total: 450	per_label:   90	  90	   0	  90	  90	  90	   0	   0	   0	   0
-partition: 0027	total: 450	per_label:    0	  90	   0	  90	   0	   0	  90	  90	   0	  90
-partition: 0028	total: 450	per_label:    0	   0	  90	  90	  90	  90	   0	   0	  90	   0
-partition: 0029	total: 450	per_label:    0	   0	  90	   0	   0	  90	   0	  90	  90	  90
-partition: 0030	total: 450	per_label:    0	  90	  90	   0	  90	   0	   0	  90	  90	   0
-partition: 0031	total: 450	per_label:   90	   0	  90	   0	  90	   0	   0	  90	  90	   0
-partition: 0032	total: 450	per_label:   90	   0	  90	   0	   0	  90	  90	   0	  90	   0
-partition: 0033	total: 450	per_label:    0	   0	  90	  90	  90	   0	   0	  90	  90	   0
-partition: 0034	total: 450	per_label:    0	   0	   0	  90	  90	  90	  90	   0	  90	   0
-partition: 0035	total: 450	per_label:    0	   0	  90	  90	   0	  90	   0	  90	  90	   0
-partition: 0036	total: 450	per_label:   90	   0	  90	   0	  90	   0	   0	  90	   0	  90
-partition: 0037	total: 450	per_label:    0	  90	  90	  90	  90	   0	   0	   0	  90	   0
-partition: 0038	total: 450	per_label:    0	   0	  90	   0	  90	   0	  90	  90	  90	   0
-partition: 0039	total: 450	per_label:   90	   0	   0	  90	  90	  90	   0	  90	   0	   0
-partition: 0040	total: 450	per_label:   90	   0	   0	  90	   0	  90	  90	   0	   0	  90
-partition: 0041	total: 450	per_label:   90	  90	  90	   0	   0	   0	   0	   0	  90	  90
-partition: 0042	total: 450	per_label:   90	  90	  90	   0	   0	   0	   0	  90	  90	   0
-partition: 0043	total: 450	per_label:    0	   0	   0	  90	  90	  90	   0	   0	  90	  90
-partition: 0044	total: 450	per_label:    0	  90	   0	   0	   0	  90	  90	   0	  90	  90
-partition: 0045	total: 450	per_label:    0	  90	   0	   0	   0	   0	  90	  90	  90	  90
-partition: 0046	total: 450	per_label:   90	   0	  90	  90	   0	  90	   0	  90	   0	   0
-partition: 0047	total: 450	per_label:   90	   0	   0	  90	  90	   0	   0	  90	   0	  90
-partition: 0048	total: 450	per_label:   90	   0	   0	  90	   0	   0	  90	  90	   0	  90
-partition: 0049	total: 450	per_label:   90	  90	   0	   0	   0	   0	  90	  90	   0	  90
-partition: 0050	total: 450	per_label:    0	  90	  90	  90	   0	  90	   0	   0	   0	  90
-partition: 0051	total: 450	per_label:    0	   0	  90	   0	  90	   0	  90	   0	  90	  90
-partition: 0052	total: 450	per_label:   90	  90	   0	   0	  90	  90	   0	   0	  90	   0
-partition: 0053	total: 450	per_label:   90	   0	   0	   0	  90	  90	   0	  90	  90	   0
-partition: 0054	total: 450	per_label:    0	  90	  90	  90	   0	  90	  90	   0	   0	   0
-partition: 0055	total: 450	per_label:   90	  90	   0	   0	   0	  90	  90	   0	   0	  90
-partition: 0056	total: 450	per_label:   90	   0	   0	  90	   0	   0	  90	   0	  90	  90
-partition: 0057	total: 450	per_label:   90	  90	  90	   0	   0	   0	   0	   0	  90	  90
-partition: 0058	total: 450	per_label:    0	   0	   0	   0	  90	  90	  90	  90	  90	   0
-partition: 0059	total: 450	per_label:   90	  90	  90	   0	   0	   0	  90	  90	   0	   0
-partition: 0060	total: 450	per_label:    0	  90	  90	   0	  90	   0	   0	  90	  90	   0
-partition: 0061	total: 450	per_label:   90	  90	   0	   0	  90	   0	  90	  90	   0	   0
-partition: 0062	total: 450	per_label:    0	  90	  90	   0	  90	   0	   0	  90	   0	  90
-partition: 0063	total: 450	per_label:    0	  90	   0	  90	  90	   0	   0	  90	  90	   0
-partition: 0064	total: 450	per_label:   90	   0	   0	   0	  90	   0	  90	  90	   0	  90
-partition: 0065	total: 450	per_label:    0	  90	   0	   0	  90	  90	   0	  90	   0	  90
-partition: 0066	total: 450	per_label:   90	  90	  90	  90	  90	   0	   0	   0	   0	   0
-partition: 0067	total: 450	per_label:    0	  90	  90	  90	  90	   0	  90	   0	   0	   0
-partition: 0068	total: 450	per_label:    0	  90	  90	   0	   0	   0	  90	   0	  90	  90
-partition: 0069	total: 450	per_label:   90	   0	   0	  90	   0	   0	  90	  90	  90	   0
-partition: 0070	total: 450	per_label:    0	   0	   0	  90	   0	  90	  90	   0	  90	  90
-partition: 0071	total: 450	per_label:   90	  90	  90	  90	  90	   0	   0	   0	   0	   0
-partition: 0072	total: 450	per_label:    0	   0	   0	  90	  90	  90	   0	  90	   0	  90
-partition: 0073	total: 450	per_label:   90	  90	   0	   0	  90	   0	  90	   0	  90	   0
-partition: 0074	total: 450	per_label:   90	   0	  90	   0	  90	  90	   0	  90	   0	   0
-partition: 0075	total: 450	per_label:    0	   0	   0	  90	   0	  90	   0	  90	  90	  90
-partition: 0076	total: 450	per_label:   90	  90	  90	  90	   0	   0	   0	   0	  90	   0
-partition: 0077	total: 450	per_label:    0	   0	  90	   0	   0	  90	  90	   0	  90	  90
-partition: 0078	total: 450	per_label:   90	   0	   0	  90	   0	  90	   0	  90	   0	  90
-partition: 0079	total: 450	per_label:    0	  90	  90	   0	   0	  90	  90	   0	   0	  90
-partition: 0080	total: 450	per_label:   90	   0	   0	   0	  90	   0	  90	  90	   0	  90
-partition: 0081	total: 450	per_label:    0	  90	   0	  90	  90	   0	  90	   0	  90	   0
-partition: 0082	total: 450	per_label:    0	   0	  90	   0	  90	  90	  90	   0	   0	  90
-partition: 0083	total: 450	per_label:   90	   0	   0	  90	   0	   0	  90	  90	  90	   0
-partition: 0084	total: 450	per_label:   90	  90	  90	   0	   0	   0	  90	   0	  90	   0
-partition: 0085	total: 450	per_label:   90	   0	   0	  90	   0	  90	   0	  90	  90	   0
-partition: 0086	total: 450	per_label:    0	   0	  90	  90	  90	   0	  90	  90	   0	   0
-partition: 0087	total: 450	per_label:   90	   0	   0	   0	  90	  90	   0	  90	   0	  90
-partition: 0088	total: 450	per_label:    0	  90	   0	  90	   0	  90	  90	  90	   0	   0
-partition: 0089	total: 450	per_label:    0	  90	   0	  90	  90	  90	  90	   0	   0	   0
-partition: 0090	total: 450	per_label:   90	  90	   0	  90	  90	   0	   0	  90	   0	   0
-partition: 0091	total: 450	per_label:    0	   0	  90	  90	   0	  90	  90	   0	   0	  90
-partition: 0092	total: 450	per_label:   90	   0	  90	   0	   0	  90	  90	   0	   0	  90
-partition: 0093	total: 450	per_label:    0	   0	  90	  90	   0	  90	  90	   0	   0	  90
-partition: 0094	total: 450	per_label:   90	   0	  90	  90	  90	   0	   0	   0	   0	  90
-partition: 0095	total: 450	per_label:   90	   0	  90	  90	  90	   0	   0	   0	   0	  90
-partition: 0096	total: 450	per_label:   90	   0	   0	  90	  90	   0	  90	   0	   0	  90
-partition: 0097	total: 450	per_label:   90	  90	   0	  90	   0	   0	  90	   0	   0	  90
-partition: 0098	total: 450	per_label:   90	   0	   0	  90	   0	   0	  90	  90	   0	  90
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-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-06cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-06cpp.txt
deleted file mode 100644
index 3374633df..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-06cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-06cpp
-number_of_examples_per_label_per_shard
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-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-07cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-07cpp.txt
deleted file mode 100644
index fd88899ae..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-07cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-07cpp
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-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-08cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-08cpp.txt
deleted file mode 100644
index 04a025b7a..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-08cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-08cpp
-number_of_examples_per_label_per_shard
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-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-09cpp.txt b/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-09cpp.txt
deleted file mode 100644
index 03ca139f5..000000000
--- a/xain_fl/datasets/stats/datasets/cifar-10-100p-noniid-09cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: cifar-10-100p-noniid-09cpp
-number_of_examples_per_label_per_shard
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-partition: 0035	total: 450	per_label:   50	   0	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0036	total: 450	per_label:   50	  50	  50	  50	  50	  50	   0	  50	  50	  50
-partition: 0037	total: 450	per_label:    0	  50	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0038	total: 450	per_label:   50	   0	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0039	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0040	total: 450	per_label:   50	   0	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0041	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0042	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0043	total: 450	per_label:   50	   0	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0044	total: 450	per_label:   50	  50	  50	   0	  50	  50	  50	  50	  50	  50
-partition: 0045	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
-partition: 0046	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0047	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
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-partition: 0049	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
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-partition: 0052	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	   0	  50	  50
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-partition: 0055	total: 450	per_label:   50	  50	  50	   0	  50	  50	  50	  50	  50	  50
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-partition: 0057	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
-partition: 0058	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0059	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
-partition: 0060	total: 450	per_label:   50	  50	  50	  50	  50	  50	   0	  50	  50	  50
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-partition: 0062	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0063	total: 450	per_label:   50	  50	  50	  50	  50	  50	   0	  50	  50	  50
-partition: 0064	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
-partition: 0065	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
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-partition: 0067	total: 450	per_label:    0	  50	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0068	total: 450	per_label:   50	  50	  50	   0	  50	  50	  50	  50	  50	  50
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-partition: 0070	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	   0	  50	  50
-partition: 0071	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0072	total: 450	per_label:   50	  50	  50	  50	  50	  50	   0	  50	  50	  50
-partition: 0073	total: 450	per_label:    0	  50	  50	  50	  50	  50	  50	  50	  50	  50
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-partition: 0075	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0076	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0077	total: 450	per_label:    0	  50	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0078	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
-partition: 0079	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0080	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0081	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
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-partition: 0083	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
-partition: 0084	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	   0	  50	  50
-partition: 0085	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0086	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0087	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
-partition: 0088	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0089	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0090	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	   0	  50
-partition: 0091	total: 450	per_label:   50	  50	   0	  50	  50	  50	  50	  50	  50	  50
-partition: 0092	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	  50	  50	   0
-partition: 0093	total: 450	per_label:   50	  50	  50	  50	  50	   0	  50	  50	  50	  50
-partition: 0094	total: 450	per_label:    0	  50	  50	  50	  50	  50	  50	  50	  50	  50
-partition: 0095	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
-partition: 0096	total: 450	per_label:   50	  50	  50	  50	   0	  50	  50	  50	  50	  50
-partition: 0097	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	   0	  50	  50
-partition: 0098	total: 450	per_label:   50	  50	  50	  50	  50	  50	  50	   0	  50	  50
-partition: 0099	total: 450	per_label:   50	  50	  50	  50	  50	  50	   0	  50	  50	  50
-partition: val	total: 5000	per_label:  500	 500	 500	 500	 500	 500	 500	 500	 500	 500
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_000.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_000.txt
deleted file mode 100644
index 99942d3ca..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_000.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_000
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:   53	  54	  49	  55	  61	  60	  57	  61	  53	  37
-partition: 0001	total: 540	per_label:   47	  54	  50	  48	  59	  56	  48	  55	  59	  64
-partition: 0002	total: 540	per_label:   67	  56	  45	  49	  59	  48	  64	  54	  46	  52
-partition: 0003	total: 540	per_label:   53	  61	  50	  53	  54	  60	  53	  59	  47	  50
-partition: 0004	total: 540	per_label:   55	  64	  45	  56	  49	  55	  58	  53	  59	  46
-partition: 0005	total: 540	per_label:   57	  59	  48	  44	  52	  53	  62	  55	  50	  60
-partition: 0006	total: 540	per_label:   57	  55	  49	  52	  54	  54	  53	  56	  52	  58
-partition: 0007	total: 540	per_label:   56	  54	  58	  57	  54	  51	  60	  55	  46	  49
-partition: 0008	total: 540	per_label:   50	  49	  59	  53	  47	  49	  63	  55	  58	  57
-partition: 0009	total: 540	per_label:   58	  51	  63	  67	  58	  52	  50	  53	  41	  47
-partition: 0010	total: 540	per_label:   53	  48	  57	  65	  51	  56	  55	  50	  56	  49
-partition: 0011	total: 540	per_label:   55	  63	  64	  50	  53	  45	  51	  42	  69	  48
-partition: 0012	total: 540	per_label:   56	  47	  52	  50	  51	  64	  56	  49	  67	  48
-partition: 0013	total: 540	per_label:   56	  70	  48	  55	  50	  44	  43	  62	  47	  65
-partition: 0014	total: 540	per_label:   46	  59	  36	  58	  49	  56	  64	  69	  54	  49
-partition: 0015	total: 540	per_label:   47	  57	  61	  46	  57	  67	  42	  46	  42	  75
-partition: 0016	total: 540	per_label:   52	  43	  58	  48	  52	  58	  50	  62	  62	  55
-partition: 0017	total: 540	per_label:   46	  51	  53	  57	  65	  73	  46	  45	  59	  45
-partition: 0018	total: 540	per_label:   52	  50	  48	  54	  45	  64	  63	  62	  53	  49
-partition: 0019	total: 540	per_label:   60	  55	  54	  51	  50	  55	  50	  53	  48	  64
-partition: 0020	total: 540	per_label:   57	  44	  59	  64	  59	  58	  49	  42	  57	  51
-partition: 0021	total: 540	per_label:   44	  51	  53	  53	  67	  59	  45	  63	  45	  60
-partition: 0022	total: 540	per_label:   56	  58	  47	  60	  51	  47	  60	  64	  48	  49
-partition: 0023	total: 540	per_label:   65	  57	  52	  45	  56	  40	  62	  50	  59	  54
-partition: 0024	total: 540	per_label:   67	  52	  67	  64	  39	  48	  56	  55	  59	  33
-partition: 0025	total: 540	per_label:   49	  53	  55	  65	  45	  51	  60	  50	  56	  56
-partition: 0026	total: 540	per_label:   43	  60	  70	  40	  44	  54	  53	  62	  50	  64
-partition: 0027	total: 540	per_label:   58	  53	  61	  49	  62	  65	  48	  38	  46	  60
-partition: 0028	total: 540	per_label:   49	  54	  63	  54	  53	  48	  54	  53	  56	  56
-partition: 0029	total: 540	per_label:   44	  54	  49	  55	  40	  59	  55	  65	  56	  63
-partition: 0030	total: 540	per_label:   59	  52	  45	  57	  51	  46	  48	  49	  70	  63
-partition: 0031	total: 540	per_label:   47	  45	  58	  56	  50	  50	  58	  64	  55	  57
-partition: 0032	total: 540	per_label:   43	  50	  56	  58	  61	  46	  56	  53	  56	  61
-partition: 0033	total: 540	per_label:   46	  63	  49	  60	  60	  47	  53	  65	  44	  53
-partition: 0034	total: 540	per_label:   63	  55	  47	  52	  56	  47	  61	  50	  49	  60
-partition: 0035	total: 540	per_label:   56	  54	  51	  59	  56	  55	  43	  56	  55	  55
-partition: 0036	total: 540	per_label:   63	  57	  51	  51	  58	  48	  47	  56	  52	  57
-partition: 0037	total: 540	per_label:   61	  52	  58	  54	  54	  35	  66	  55	  56	  49
-partition: 0038	total: 540	per_label:   53	  56	  51	  43	  53	  64	  50	  63	  50	  57
-partition: 0039	total: 540	per_label:   53	  53	  45	  63	  49	  44	  62	  64	  43	  64
-partition: 0040	total: 540	per_label:   59	  45	  57	  55	  56	  54	  50	  48	  47	  69
-partition: 0041	total: 540	per_label:   59	  51	  54	  54	  50	  47	  58	  61	  51	  55
-partition: 0042	total: 540	per_label:   48	  57	  56	  70	  46	  49	  42	  48	  62	  62
-partition: 0043	total: 540	per_label:   47	  58	  40	  51	  46	  51	  62	  50	  68	  67
-partition: 0044	total: 540	per_label:   72	  54	  59	  54	  45	  41	  54	  45	  56	  60
-partition: 0045	total: 540	per_label:   45	  66	  64	  53	  59	  56	  58	  46	  35	  58
-partition: 0046	total: 540	per_label:   56	  68	  56	  50	  66	  56	  43	  47	  54	  44
-partition: 0047	total: 540	per_label:   64	  58	  48	  47	  52	  50	  47	  57	  64	  53
-partition: 0048	total: 540	per_label:   45	  51	  51	  58	  38	  53	  68	  60	  58	  58
-partition: 0049	total: 540	per_label:   60	  47	  49	  47	  73	  60	  50	  53	  48	  53
-partition: 0050	total: 540	per_label:   72	  53	  39	  46	  56	  46	  60	  57	  63	  48
-partition: 0051	total: 540	per_label:   51	  64	  52	  57	  50	  64	  63	  55	  46	  38
-partition: 0052	total: 540	per_label:   64	  43	  52	  55	  60	  62	  50	  58	  50	  46
-partition: 0053	total: 540	per_label:   43	  47	  58	  51	  61	  41	  58	  50	  79	  52
-partition: 0054	total: 540	per_label:   52	  56	  63	  54	  51	  62	  50	  50	  63	  39
-partition: 0055	total: 540	per_label:   47	  59	  58	  51	  51	  54	  52	  57	  53	  58
-partition: 0056	total: 540	per_label:   59	  60	  54	  48	  52	  50	  63	  62	  45	  47
-partition: 0057	total: 540	per_label:   52	  51	  56	  63	  44	  63	  62	  50	  57	  42
-partition: 0058	total: 540	per_label:   51	  42	  50	  54	  74	  55	  60	  59	  49	  46
-partition: 0059	total: 540	per_label:   55	  43	  60	  52	  57	  65	  48	  41	  47	  72
-partition: 0060	total: 540	per_label:   52	  67	  59	  49	  54	  55	  51	  53	  45	  55
-partition: 0061	total: 540	per_label:   54	  43	  57	  60	  58	  49	  46	  64	  59	  50
-partition: 0062	total: 540	per_label:   60	  52	  56	  53	  62	  43	  53	  57	  57	  47
-partition: 0063	total: 540	per_label:   54	  47	  70	  45	  53	  57	  60	  46	  47	  61
-partition: 0064	total: 540	per_label:   40	  52	  44	  48	  56	  61	  66	  44	  75	  54
-partition: 0065	total: 540	per_label:   58	  59	  45	  62	  67	  54	  48	  45	  48	  54
-partition: 0066	total: 540	per_label:   54	  58	  56	  44	  62	  51	  59	  61	  42	  53
-partition: 0067	total: 540	per_label:   60	  51	  54	  51	  59	  57	  43	  58	  51	  56
-partition: 0068	total: 540	per_label:   44	  40	  56	  63	  47	  58	  68	  61	  50	  53
-partition: 0069	total: 540	per_label:   59	  48	  51	  59	  53	  60	  46	  41	  68	  55
-partition: 0070	total: 540	per_label:   56	  62	  46	  61	  54	  53	  51	  45	  65	  47
-partition: 0071	total: 540	per_label:   62	  58	  51	  58	  50	  56	  44	  58	  51	  52
-partition: 0072	total: 540	per_label:   57	  56	  57	  63	  56	  38	  52	  53	  56	  52
-partition: 0073	total: 540	per_label:   47	  65	  48	  61	  48	  53	  60	  60	  48	  50
-partition: 0074	total: 540	per_label:   51	  51	  47	  58	  47	  68	  55	  52	  53	  58
-partition: 0075	total: 540	per_label:   55	  44	  69	  55	  45	  47	  62	  54	  51	  58
-partition: 0076	total: 540	per_label:   51	  63	  65	  50	  51	  43	  49	  56	  67	  45
-partition: 0077	total: 540	per_label:   63	  51	  52	  52	  56	  43	  51	  50	  55	  67
-partition: 0078	total: 540	per_label:   50	  59	  47	  47	  64	  54	  55	  56	  53	  55
-partition: 0079	total: 540	per_label:   53	  57	  60	  51	  60	  56	  57	  40	  46	  60
-partition: 0080	total: 540	per_label:   51	  52	  59	  44	  50	  49	  53	  71	  53	  58
-partition: 0081	total: 540	per_label:   54	  48	  67	  67	  53	  56	  45	  49	  43	  58
-partition: 0082	total: 540	per_label:   63	  49	  58	  46	  50	  60	  57	  54	  58	  45
-partition: 0083	total: 540	per_label:   50	  57	  52	  60	  50	  49	  52	  65	  57	  48
-partition: 0084	total: 540	per_label:   56	  60	  49	  52	  52	  46	  47	  57	  53	  68
-partition: 0085	total: 540	per_label:   50	  40	  69	  62	  57	  63	  41	  47	  49	  62
-partition: 0086	total: 540	per_label:   47	  49	  60	  57	  68	  55	  51	  52	  53	  48
-partition: 0087	total: 540	per_label:   61	  60	  48	  47	  49	  66	  53	  42	  62	  52
-partition: 0088	total: 540	per_label:   52	  59	  52	  46	  59	  62	  50	  50	  58	  52
-partition: 0089	total: 540	per_label:   59	  51	  51	  48	  47	  66	  53	  56	  67	  42
-partition: 0090	total: 540	per_label:   58	  46	  50	  58	  64	  57	  50	  46	  50	  61
-partition: 0091	total: 540	per_label:   63	  43	  52	  62	  54	  58	  58	  52	  53	  45
-partition: 0092	total: 540	per_label:   45	  60	  43	  61	  68	  48	  52	  61	  53	  49
-partition: 0093	total: 540	per_label:   53	  63	  61	  51	  44	  58	  41	  71	  53	  45
-partition: 0094	total: 540	per_label:   48	  49	  58	  56	  62	  65	  55	  52	  39	  56
-partition: 0095	total: 540	per_label:   48	  59	  57	  42	  47	  63	  59	  53	  49	  63
-partition: 0096	total: 540	per_label:   46	  61	  48	  59	  46	  59	  49	  48	  71	  53
-partition: 0097	total: 540	per_label:   52	  54	  50	  41	  58	  56	  69	  52	  55	  53
-partition: 0098	total: 540	per_label:   58	  47	  65	  57	  42	  50	  59	  51	  59	  52
-partition: 0099	total: 540	per_label:   53	  64	  51	  54	  57	  48	  58	  50	  53	  52
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_005.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_005.txt
deleted file mode 100644
index ea648b671..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_005.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_005
-number_of_examples_per_label_per_shard
-partition: 0000	total: 417	per_label:   42	  41	  34	  42	  49	  44	  42	  50	  41	  32
-partition: 0001	total: 420	per_label:   39	  44	  40	  41	  40	  50	  43	  39	  43	  41
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-partition: 0003	total: 424	per_label:   50	  47	  36	  35	  52	  39	  38	  37	  41	  49
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-partition: 0036	total: 500	per_label:   55	  44	  59	  51	  40	  39	  46	  45	  58	  63
-partition: 0037	total: 502	per_label:   39	  44	  51	  53	  52	  43	  56	  57	  52	  55
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-partition: 0041	total: 512	per_label:   51	  54	  50	  55	  55	  52	  42	  51	  51	  51
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-partition: 0044	total: 520	per_label:   50	  53	  47	  41	  50	  60	  53	  65	  47	  54
-partition: 0045	total: 523	per_label:   53	  53	  48	  59	  49	  45	  61	  56	  36	  63
-partition: 0046	total: 525	per_label:   56	  46	  55	  57	  50	  45	  49	  51	  53	  63
-partition: 0047	total: 528	per_label:   56	  51	  50	  55	  47	  51	  53	  57	  48	  60
-partition: 0048	total: 530	per_label:   51	  51	  55	  60	  52	  54	  41	  51	  57	  58
-partition: 0049	total: 533	per_label:   51	  54	  48	  59	  41	  44	  65	  49	  64	  58
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-partition: 0057	total: 555	per_label:   56	  66	  54	  60	  50	  64	  66	  57	  47	  35
-partition: 0058	total: 558	per_label:   66	  45	  50	  55	  58	  65	  53	  58	  53	  55
-partition: 0059	total: 560	per_label:   43	  45	  63	  53	  67	  45	  61	  53	  78	  52
-partition: 0060	total: 563	per_label:   54	  60	  63	  55	  54	  64	  51	  53	  68	  41
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-partition: 0062	total: 569	per_label:   60	  62	  56	  50	  57	  53	  65	  68	  46	  52
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-partition: 0064	total: 575	per_label:   57	  44	  51	  57	  80	  66	  57	  57	  55	  51
-partition: 0065	total: 577	per_label:   54	  56	  75	  60	  55	  59	  51	  49	  43	  75
-partition: 0066	total: 580	per_label:   63	  67	  56	  48	  57	  63	  60	  62	  52	  52
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-partition: 0069	total: 589	per_label:   55	  58	  61	  45	  56	  62	  70	  47	  72	  63
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-partition: 0081	total: 625	per_label:   70	  61	  61	  60	  68	  53	  59	  57	  61	  75
-partition: 0082	total: 629	per_label:   56	  65	  55	  49	  74	  63	  64	  66	  65	  72
-partition: 0083	total: 632	per_label:   68	  62	  67	  64	  66	  63	  65	  56	  56	  65
-partition: 0084	total: 635	per_label:   56	  64	  79	  67	  57	  60	  56	  76	  48	  72
-partition: 0085	total: 638	per_label:   71	  55	  74	  61	  59	  75	  65	  57	  70	  51
-partition: 0086	total: 641	per_label:   63	  68	  61	  68	  61	  56	  64	  74	  67	  59
-partition: 0087	total: 645	per_label:   71	  66	  60	  64	  59	  54	  55	  68	  68	  80
-partition: 0088	total: 648	per_label:   51	  58	  78	  75	  73	  76	  55	  60	  57	  65
-partition: 0089	total: 651	per_label:   59	  67	  64	  59	  74	  76	  59	  58	  71	  64
-partition: 0090	total: 654	per_label:   72	  67	  67	  58	  70	  73	  61	  58	  67	  61
-partition: 0091	total: 657	per_label:   74	  62	  61	  56	  56	  85	  64	  62	  83	  54
-partition: 0092	total: 661	per_label:   72	  54	  56	  75	  71	  74	  63	  63	  57	  76
-partition: 0093	total: 664	per_label:   73	  67	  63	  74	  76	  54	  63	  67	  72	  55
-partition: 0094	total: 667	per_label:   55	  74	  71	  64	  68	  66	  63	  82	  65	  59
-partition: 0095	total: 671	per_label:   64	  64	  69	  72	  74	  83	  65	  66	  49	  65
-partition: 0096	total: 674	per_label:   58	  71	  71	  56	  62	  82	  69	  66	  67	  72
-partition: 0097	total: 677	per_label:   60	  78	  57	  68	  61	  63	  76	  66	  81	  67
-partition: 0098	total: 681	per_label:   72	  60	  81	  61	  57	  74	  78	  56	  74	  68
-partition: 0099	total: 684	per_label:   68	  77	  66	  70	  71	  60	  72	  68	  66	  66
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_010.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_010.txt
deleted file mode 100644
index dc318bfb0..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_010.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_010
-number_of_examples_per_label_per_shard
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-partition: 0001	total: 320	per_label:   28	  29	  30	  30	  38	  39	  31	  31	  42	  22
-partition: 0002	total: 323	per_label:   28	  32	  29	  33	  33	  33	  33	  35	  27	  40
-partition: 0003	total: 326	per_label:   37	  30	  34	  30	  32	  27	  45	  37	  26	  28
-partition: 0004	total: 329	per_label:   38	  38	  23	  27	  42	  33	  27	  31	  36	  34
-partition: 0005	total: 333	per_label:   35	  42	  33	  32	  36	  34	  35	  30	  30	  26
-partition: 0006	total: 336	per_label:   34	  37	  25	  35	  29	  38	  29	  39	  38	  32
-partition: 0007	total: 339	per_label:   37	  40	  33	  35	  31	  38	  35	  36	  28	  26
-partition: 0008	total: 343	per_label:   33	  32	  30	  27	  36	  27	  39	  34	  42	  43
-partition: 0009	total: 346	per_label:   32	  42	  31	  27	  33	  41	  40	  35	  26	  39
-partition: 0010	total: 350	per_label:   39	  38	  32	  40	  30	  35	  37	  40	  30	  29
-partition: 0011	total: 353	per_label:   39	  30	  31	  35	  32	  41	  40	  35	  34	  36
-partition: 0012	total: 357	per_label:   39	  37	  47	  35	  39	  25	  35	  33	  30	  37
-partition: 0013	total: 360	per_label:   27	  30	  36	  39	  33	  33	  50	  34	  40	  38
-partition: 0014	total: 364	per_label:   35	  36	  40	  39	  42	  30	  31	  38	  35	  38
-partition: 0015	total: 368	per_label:   46	  37	  41	  45	  30	  42	  38	  37	  26	  26
-partition: 0016	total: 371	per_label:   40	  31	  38	  44	  39	  36	  33	  31	  44	  35
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-partition: 0018	total: 379	per_label:   37	  35	  41	  38	  38	  30	  41	  38	  47	  34
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-partition: 0020	total: 386	per_label:   39	  56	  42	  43	  35	  37	  20	  38	  32	  44
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-partition: 0023	total: 398	per_label:   34	  42	  48	  27	  42	  48	  37	  31	  27	  62
-partition: 0024	total: 402	per_label:   33	  35	  40	  46	  35	  51	  31	  41	  49	  41
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-partition: 0027	total: 414	per_label:   37	  36	  42	  40	  36	  46	  53	  48	  38	  38
-partition: 0028	total: 418	per_label:   48	  42	  31	  43	  38	  45	  37	  47	  36	  51
-partition: 0029	total: 423	per_label:   44	  42	  49	  47	  38	  48	  40	  32	  44	  39
-partition: 0030	total: 427	per_label:   45	  36	  45	  44	  58	  44	  34	  39	  39	  43
-partition: 0031	total: 431	per_label:   33	  40	  43	  44	  47	  47	  39	  52	  37	  49
-partition: 0032	total: 435	per_label:   44	  45	  40	  48	  40	  39	  46	  49	  37	  47
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-partition: 0036	total: 453	per_label:   35	  42	  49	  50	  38	  45	  55	  47	  46	  46
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-partition: 0039	total: 467	per_label:   42	  43	  67	  42	  49	  46	  43	  42	  46	  47
-partition: 0040	total: 472	per_label:   35	  52	  44	  49	  36	  50	  50	  53	  51	  52
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-partition: 0059	total: 570	per_label:   67	  60	  47	  52	  60	  53	  53	  59	  65	  54
-partition: 0060	total: 575	per_label:   49	  57	  56	  60	  42	  56	  72	  60	  63	  60
-partition: 0061	total: 581	per_label:   64	  51	  51	  48	  79	  64	  51	  61	  55	  57
-partition: 0062	total: 587	per_label:   78	  61	  46	  54	  55	  52	  66	  61	  63	  51
-partition: 0063	total: 593	per_label:   60	  67	  53	  54	  57	  73	  67	  61	  51	  50
-partition: 0064	total: 599	per_label:   60	  45	  60	  69	  67	  55	  60	  62	  72	  49
-partition: 0065	total: 605	per_label:   46	  58	  75	  56	  68	  51	  59	  60	  81	  51
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-partition: 0067	total: 617	per_label:   59	  66	  62	  55	  62	  66	  65	  78	  54	  50
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-partition: 0069	total: 629	per_label:   60	  50	  61	  64	  84	  66	  72	  65	  57	  50
-partition: 0070	total: 636	per_label:   62	  55	  77	  65	  66	  70	  57	  50	  50	  84
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-partition: 0072	total: 648	per_label:   65	  51	  66	  69	  77	  57	  60	  61	  74	  68
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-partition: 0075	total: 668	per_label:   66	  67	  56	  67	  88	  70	  63	  60	  61	  70
-partition: 0076	total: 675	per_label:   73	  67	  68	  62	  73	  63	  61	  78	  61	  69
-partition: 0077	total: 682	per_label:   65	  60	  78	  73	  58	  72	  77	  72	  59	  68
-partition: 0078	total: 688	per_label:   69	  56	  62	  74	  74	  77	  68	  56	  80	  72
-partition: 0079	total: 695	per_label:   77	  76	  62	  82	  61	  72	  63	  64	  81	  57
-partition: 0080	total: 702	per_label:   74	  86	  67	  71	  77	  60	  60	  71	  71	  65
-partition: 0081	total: 709	per_label:   68	  77	  70	  83	  63	  63	  76	  77	  63	  69
-partition: 0082	total: 716	per_label:   66	  60	  65	  78	  60	  80	  81	  76	  68	  82
-partition: 0083	total: 724	per_label:   78	  71	  94	  72	  61	  61	  69	  70	  81	  67
-partition: 0084	total: 731	per_label:   77	  79	  73	  68	  77	  60	  70	  67	  79	  81
-partition: 0085	total: 738	per_label:   68	  77	  67	  58	  92	  72	  76	  75	  71	  82
-partition: 0086	total: 746	per_label:   73	  76	  84	  73	  71	  77	  74	  73	  67	  78
-partition: 0087	total: 753	per_label:   73	  70	  95	  89	  70	  75	  67	  75	  65	  74
-partition: 0088	total: 760	per_label:   83	  72	  75	  68	  74	  70	  79	  86	  84	  69
-partition: 0089	total: 768	per_label:   80	  85	  68	  78	  73	  75	  69	  79	  74	  87
-partition: 0090	total: 776	per_label:   66	  66	  93	  89	  82	  85	  66	  75	  74	  80
-partition: 0091	total: 784	per_label:   79	  76	  76	  72	  86	  97	  72	  66	  85	  75
-partition: 0092	total: 791	per_label:   84	  87	  80	  67	  74	  87	  73	  72	  91	  76
-partition: 0093	total: 799	per_label:   88	  71	  75	  78	  91	  89	  76	  74	  81	  76
-partition: 0094	total: 807	per_label:   93	  73	  73	  90	  81	  79	  81	  81	  84	  72
-partition: 0095	total: 815	per_label:   65	  90	  82	  84	  89	  81	  75	  97	  78	  74
-partition: 0096	total: 824	per_label:   84	  73	  88	  88	  78	 105	  84	  84	  60	  80
-partition: 0097	total: 832	per_label:   63	  97	  82	  72	  81	  89	  74	  82	  99	  93
-partition: 0098	total: 840	per_label:   87	  78	  80	  79	  77	  90	 112	  74	  84	  79
-partition: 0099	total: 849	per_label:   84	  96	  90	  83	  83	  73	  86	  82	  90	  82
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_015.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_015.txt
deleted file mode 100644
index 53fe868f9..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_015.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_015
-number_of_examples_per_label_per_shard
-partition: 0000	total: 236	per_label:   23	  26	  18	  20	  27	  25	  28	  25	  19	  25
-partition: 0001	total: 239	per_label:   27	  22	  20	  28	  28	  28	  23	  28	  27	   8
-partition: 0002	total: 243	per_label:   23	  24	  28	  24	  23	  25	  22	  19	  33	  22
-partition: 0003	total: 247	per_label:   17	  26	  20	  24	  27	  25	  24	  30	  18	  36
-partition: 0004	total: 250	per_label:   29	  19	  28	  20	  24	  21	  36	  33	  21	  19
-partition: 0005	total: 254	per_label:   30	  34	  21	  24	  32	  24	  24	  18	  23	  24
-partition: 0006	total: 258	per_label:   29	  25	  21	  19	  32	  20	  28	  25	  26	  33
-partition: 0007	total: 262	per_label:   28	  34	  24	  27	  24	  35	  23	  25	  26	  16
-partition: 0008	total: 266	per_label:   22	  29	  18	  29	  27	  30	  23	  34	  28	  26
-partition: 0009	total: 270	per_label:   31	  30	  29	  28	  19	  31	  29	  29	  27	  17
-partition: 0010	total: 274	per_label:   27	  26	  23	  27	  27	  26	  29	  25	  28	  36
-partition: 0011	total: 278	per_label:   28	  33	  21	  22	  33	  20	  33	  31	  28	  29
-partition: 0012	total: 282	per_label:   28	  35	  30	  19	  23	  35	  29	  28	  25	  30
-partition: 0013	total: 286	per_label:   32	  30	  26	  35	  27	  30	  31	  33	  20	  22
-partition: 0014	total: 291	per_label:   30	  23	  27	  29	  31	  32	  30	  27	  29	  33
-partition: 0015	total: 295	per_label:   29	  32	  32	  28	  26	  28	  36	  30	  23	  31
-partition: 0016	total: 299	per_label:   37	  25	  38	  32	  28	  21	  30	  30	  32	  26
-partition: 0017	total: 304	per_label:   23	  30	  24	  28	  30	  28	  38	  32	  35	  36
-partition: 0018	total: 308	per_label:   24	  29	  38	  38	  39	  25	  29	  27	  26	  33
-partition: 0019	total: 313	per_label:   42	  32	  35	  40	  23	  34	  32	  31	  23	  21
-partition: 0020	total: 318	per_label:   31	  27	  30	  34	  34	  34	  30	  28	  42	  28
-partition: 0021	total: 322	per_label:   33	  33	  40	  33	  33	  26	  32	  27	  31	  34
-partition: 0022	total: 327	per_label:   33	  39	  37	  35	  26	  33	  28	  29	  42	  25
-partition: 0023	total: 332	per_label:   32	  25	  30	  33	  33	  30	  44	  31	  37	  37
-partition: 0024	total: 337	per_label:   36	  34	  43	  34	  33	  39	  23	  29	  40	  26
-partition: 0025	total: 342	per_label:   36	  50	  27	  33	  35	  37	  23	  36	  26	  39
-partition: 0026	total: 347	per_label:   32	  32	  23	  39	  28	  29	  45	  41	  39	  39
-partition: 0027	total: 353	per_label:   29	  43	  28	  33	  35	  36	  36	  51	  33	  29
-partition: 0028	total: 358	per_label:   35	  35	  44	  25	  35	  43	  33	  28	  25	  55
-partition: 0029	total: 363	per_label:   23	  37	  39	  40	  36	  45	  26	  34	  43	  40
-partition: 0030	total: 369	per_label:   43	  29	  34	  31	  34	  40	  37	  43	  42	  36
-partition: 0031	total: 374	per_label:   30	  33	  35	  42	  46	  51	  28	  31	  41	  37
-partition: 0032	total: 380	per_label:   41	  36	  32	  29	  38	  47	  44	  46	  36	  31
-partition: 0033	total: 386	per_label:   32	  35	  38	  44	  34	  44	  44	  46	  35	  34
-partition: 0034	total: 391	per_label:   47	  43	  41	  38	  36	  36	  37	  32	  34	  47
-partition: 0035	total: 397	per_label:   37	  37	  49	  47	  42	  47	  33	  29	  40	  36
-partition: 0036	total: 403	per_label:   41	  31	  38	  40	  49	  44	  36	  41	  41	  42
-partition: 0037	total: 409	per_label:   32	  42	  42	  45	  46	  43	  31	  51	  32	  45
-partition: 0038	total: 415	per_label:   44	  46	  32	  47	  39	  32	  48	  45	  38	  44
-partition: 0039	total: 422	per_label:   54	  49	  38	  29	  44	  33	  51	  40	  40	  44
-partition: 0040	total: 428	per_label:   47	  40	  54	  47	  33	  39	  50	  42	  48	  28
-partition: 0041	total: 434	per_label:   55	  44	  45	  57	  34	  34	  37	  43	  52	  33
-partition: 0042	total: 441	per_label:   34	  40	  49	  48	  37	  44	  56	  45	  44	  44
-partition: 0043	total: 448	per_label:   39	  49	  58	  31	  39	  41	  42	  53	  41	  55
-partition: 0044	total: 454	per_label:   50	  42	  41	  43	  52	  56	  43	  34	  39	  54
-partition: 0045	total: 461	per_label:   42	  43	  68	  43	  46	  46	  41	  41	  46	  45
-partition: 0046	total: 468	per_label:   33	  53	  45	  47	  38	  49	  48	  52	  52	  51
-partition: 0047	total: 475	per_label:   48	  47	  29	  50	  48	  51	  45	  53	  52	  52
-partition: 0048	total: 482	per_label:   54	  43	  57	  50	  38	  36	  45	  42	  58	  59
-partition: 0049	total: 490	per_label:   39	  39	  51	  53	  50	  46	  52	  58	  46	  56
-partition: 0050	total: 497	per_label:   39	  47	  46	  61	  51	  41	  53	  47	  58	  54
-partition: 0051	total: 504	per_label:   46	  63	  47	  47	  57	  44	  49	  62	  40	  49
-partition: 0052	total: 512	per_label:   58	  50	  47	  50	  54	  45	  59	  49	  44	  56
-partition: 0053	total: 520	per_label:   55	  52	  49	  58	  54	  52	  41	  52	  55	  52
-partition: 0054	total: 527	per_label:   64	  58	  47	  49	  54	  49	  44	  57	  48	  57
-partition: 0055	total: 535	per_label:   56	  47	  61	  51	  58	  36	  65	  54	  57	  50
-partition: 0056	total: 543	per_label:   54	  55	  49	  47	  53	  60	  54	  65	  50	  56
-partition: 0057	total: 552	per_label:   55	  56	  48	  61	  50	  48	  63	  65	  40	  66
-partition: 0058	total: 560	per_label:   61	  50	  57	  60	  56	  56	  51	  48	  53	  68
-partition: 0059	total: 568	per_label:   64	  52	  58	  57	  55	  49	  59	  62	  52	  60
-partition: 0060	total: 577	per_label:   51	  59	  61	  70	  49	  49	  50	  53	  69	  66
-partition: 0061	total: 585	per_label:   52	  62	  49	  55	  50	  56	  63	  52	  74	  72
-partition: 0062	total: 594	per_label:   72	  68	  67	  59	  49	  50	  57	  50	  54	  68
-partition: 0063	total: 603	per_label:   60	  77	  64	  61	  70	  62	  59	  50	  45	  55
-partition: 0064	total: 612	per_label:   71	  66	  55	  49	  72	  60	  49	  61	  75	  54
-partition: 0065	total: 621	per_label:   55	  64	  58	  67	  52	  57	  74	  66	  63	  65
-partition: 0066	total: 631	per_label:   69	  59	  54	  58	  69	  74	  60	  66	  59	  63
-partition: 0067	total: 640	per_label:   78	  58	  55	  59	  67	  55	  73	  68	  69	  58
-partition: 0068	total: 650	per_label:   72	  76	  58	  59	  61	  80	  72	  65	  53	  54
-partition: 0069	total: 659	per_label:   62	  52	  64	  74	  77	  61	  68	  65	  84	  52
-partition: 0070	total: 669	per_label:   54	  68	  81	  62	  71	  63	  64	  71	  80	  55
-partition: 0071	total: 679	per_label:   66	  66	  75	  68	  61	  73	  67	  64	  69	  70
-partition: 0072	total: 690	per_label:   72	  75	  67	  61	  66	  70	  79	  78	  60	  62
-partition: 0073	total: 700	per_label:   72	  68	  68	  75	  71	  74	  81	  69	  70	  52
-partition: 0074	total: 710	per_label:   68	  50	  77	  73	  82	  82	  68	  61	  64	  85
-partition: 0075	total: 721	per_label:   73	  87	  83	  64	  73	  73	  64	  72	  63	  69
-partition: 0076	total: 732	per_label:   71	  59	  76	  80	  87	  60	  73	  72	  81	  73
-partition: 0077	total: 743	per_label:   76	  66	  87	  68	  74	  76	  75	  80	  64	  77
-partition: 0078	total: 754	per_label:   66	  77	  61	  69	  76	  87	  83	  61	  97	  77
-partition: 0079	total: 765	per_label:   80	  80	  71	  74	  97	  71	  81	  76	  63	  72
-partition: 0080	total: 777	per_label:   81	  77	  83	  80	  81	  76	  72	  85	  68	  74
-partition: 0081	total: 789	per_label:   71	  58	  79	  88	  75	  87	  79	  74	  87	  91
-partition: 0082	total: 800	per_label:   86	  87	  66	  87	  80	  82	  72	  71	  99	  70
-partition: 0083	total: 812	per_label:   88	  96	  81	  88	  78	  78	  74	  80	  79	  70
-partition: 0084	total: 825	per_label:   78	  87	  82	  98	  77	  72	  84	  89	  79	  79
-partition: 0085	total: 837	per_label:   83	  74	  83	  88	  74	  89	  93	  85	  74	  94
-partition: 0086	total: 850	per_label:   79	  94	 107	  79	  73	  76	  78	  87	  99	  78
-partition: 0087	total: 862	per_label:   96	  85	  74	  75	 102	  75	  87	  80	  90	  98
-partition: 0088	total: 875	per_label:   87	  84	  90	  83	  95	  88	  90	  82	  78	  98
-partition: 0089	total: 888	per_label:   80	  87	 114	  98	  79	  90	  81	  93	  76	  90
-partition: 0090	total: 902	per_label:   98	  87	  89	  87	  87	  85	  90	  98	  98	  83
-partition: 0091	total: 915	per_label:  102	  83	  88	  90	  94	  90	  81	  94	  91	 102
-partition: 0092	total: 929	per_label:   71	  90	 115	 101	 103	 103	  79	  83	  87	  97
-partition: 0093	total: 943	per_label:  101	 104	  82	  82	  95	 113	  90	  83	 103	  90
-partition: 0094	total: 957	per_label:  108	  89	  89	  89	  98	 111	  90	  93	 106	  84
-partition: 0095	total: 971	per_label:  102	  88	  87	 111	 102	  92	  98	 100	  99	  92
-partition: 0096	total: 986	per_label:   86	 109	  98	 103	  98	 107	  93	 116	  91	  85
-partition: 0097	total: 1001	per_label:   90	  99	 110	  86	 102	 122	 100	  94	  90	 108
-partition: 0098	total: 1016	per_label:   95	 108	  94	  93	  95	 105	 113	  94	 116	 103
-partition: 0099	total: 1031	per_label:  105	 107	 111	 107	  95	  92	 113	  97	 105	  99
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_020.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_020.txt
deleted file mode 100644
index d8ac5a1ca..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_020.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_020
-number_of_examples_per_label_per_shard
-partition: 0000	total: 173	per_label:   17	  20	  11	  16	  19	  19	  23	  19	  12	  17
-partition: 0001	total: 176	per_label:   19	  17	  19	  20	  20	  16	  13	  20	  19	  13
-partition: 0002	total: 180	per_label:   15	  16	  17	  18	  22	  24	  21	  21	  20	   6
-partition: 0003	total: 183	per_label:   22	  18	  18	  18	  16	  18	  15	  12	  27	  19
-partition: 0004	total: 187	per_label:   12	  21	  15	  16	  18	  23	  20	  26	  11	  25
-partition: 0005	total: 191	per_label:   18	  17	  21	  15	  26	  17	  13	  19	  24	  21
-partition: 0006	total: 194	per_label:   23	  17	  19	  22	  16	  12	  36	  22	   9	  18
-partition: 0007	total: 198	per_label:   25	  26	  15	  17	  24	  22	  18	  15	  20	  16
-partition: 0008	total: 202	per_label:   22	  19	  18	  15	  25	  16	  17	  21	  20	  29
-partition: 0009	total: 206	per_label:   23	  25	  17	  17	  28	  19	  24	  18	  21	  14
-partition: 0010	total: 211	per_label:   18	  25	  17	  22	  15	  32	  17	  29	  18	  18
-partition: 0011	total: 215	per_label:   26	  27	  15	  25	  18	  24	  18	  18	  27	  17
-partition: 0012	total: 219	per_label:   19	  22	  26	  23	  18	  23	  26	  27	  21	  14
-partition: 0013	total: 223	per_label:   22	  22	  15	  24	  23	  21	  22	  22	  21	  31
-partition: 0014	total: 228	per_label:   23	  26	  23	  14	  27	  15	  24	  25	  28	  23
-partition: 0015	total: 233	per_label:   25	  27	  20	  22	  17	  28	  33	  21	  13	  27
-partition: 0016	total: 237	per_label:   23	  28	  24	  21	  25	  26	  23	  24	  23	  20
-partition: 0017	total: 242	per_label:   27	  23	  19	  26	  20	  24	  25	  28	  22	  28
-partition: 0018	total: 247	per_label:   26	  22	  25	  25	  28	  28	  24	  24	  24	  21
-partition: 0019	total: 252	per_label:   24	  26	  28	  22	  21	  28	  32	  25	  19	  27
-partition: 0020	total: 257	per_label:   34	  22	  32	  29	  26	  14	  22	  26	  26	  26
-partition: 0021	total: 262	per_label:   15	  23	  27	  27	  25	  25	  41	  24	  28	  27
-partition: 0022	total: 267	per_label:   28	  29	  28	  30	  27	  19	  24	  26	  29	  27
-partition: 0023	total: 272	per_label:   30	  25	  29	  29	  35	  29	  25	  28	  18	  24
-partition: 0024	total: 278	per_label:   30	  31	  28	  34	  20	  33	  29	  29	  25	  19
-partition: 0025	total: 284	per_label:   32	  21	  33	  35	  28	  26	  24	  21	  35	  29
-partition: 0026	total: 289	per_label:   26	  29	  32	  27	  31	  23	  30	  25	  34	  32
-partition: 0027	total: 295	per_label:   29	  36	  35	  32	  23	  32	  28	  25	  36	  19
-partition: 0028	total: 301	per_label:   28	  24	  28	  27	  33	  24	  36	  32	  32	  37
-partition: 0029	total: 307	per_label:   36	  27	  34	  30	  26	  42	  27	  25	  40	  20
-partition: 0030	total: 313	per_label:   30	  50	  34	  34	  28	  28	  15	  31	  27	  36
-partition: 0031	total: 319	per_label:   35	  33	  23	  35	  29	  27	  33	  40	  28	  36
-partition: 0032	total: 326	per_label:   26	  33	  18	  30	  34	  31	  49	  38	  37	  30
-partition: 0033	total: 332	per_label:   30	  34	  39	  34	  30	  40	  27	  38	  25	  35
-partition: 0034	total: 339	per_label:   28	  40	  32	  27	  37	  42	  25	  28	  27	  53
-partition: 0035	total: 346	per_label:   32	  29	  38	  32	  29	  43	  31	  37	  40	  35
-partition: 0036	total: 353	per_label:   31	  25	  33	  34	  40	  42	  34	  38	  43	  33
-partition: 0037	total: 360	per_label:   32	  37	  37	  38	  47	  41	  30	  31	  36	  31
-partition: 0038	total: 367	per_label:   35	  32	  37	  33	  28	  44	  46	  43	  35	  34
-partition: 0039	total: 374	per_label:   39	  37	  32	  33	  35	  46	  37	  44	  35	  36
-partition: 0040	total: 382	per_label:   43	  40	  37	  42	  35	  37	  33	  34	  34	  47
-partition: 0041	total: 389	per_label:   38	  32	  45	  49	  41	  44	  35	  28	  42	  35
-partition: 0042	total: 397	per_label:   38	  35	  40	  39	  47	  46	  36	  38	  38	  40
-partition: 0043	total: 405	per_label:   33	  44	  38	  41	  48	  39	  30	  56	  33	  43
-partition: 0044	total: 413	per_label:   46	  41	  35	  46	  41	  32	  51	  44	  36	  41
-partition: 0045	total: 422	per_label:   52	  48	  41	  33	  42	  31	  49	  37	  42	  47
-partition: 0046	total: 430	per_label:   48	  44	  52	  46	  31	  38	  50	  43	  48	  30
-partition: 0047	total: 439	per_label:   54	  44	  45	  58	  35	  36	  40	  45	  50	  32
-partition: 0048	total: 447	per_label:   36	  45	  54	  44	  34	  47	  53	  45	  45	  44
-partition: 0049	total: 456	per_label:   38	  45	  56	  34	  44	  48	  42	  48	  43	  58
-partition: 0050	total: 465	per_label:   50	  44	  50	  45	  53	  56	  41	  35	  40	  51
-partition: 0051	total: 475	per_label:   44	  50	  60	  46	  47	  43	  46	  46	  48	  45
-partition: 0052	total: 484	per_label:   37	  45	  47	  50	  36	  47	  56	  55	  49	  62
-partition: 0053	total: 494	per_label:   48	  51	  35	  48	  53	  49	  43	  52	  61	  54
-partition: 0054	total: 504	per_label:   56	  51	  55	  55	  35	  43	  44	  53	  53	  59
-partition: 0055	total: 514	per_label:   36	  38	  50	  59	  61	  42	  54	  54	  60	  60
-partition: 0056	total: 524	per_label:   47	  50	  50	  58	  57	  53	  58	  60	  46	  45
-partition: 0057	total: 535	per_label:   56	  63	  54	  51	  65	  43	  56	  51	  40	  56
-partition: 0058	total: 545	per_label:   59	  57	  43	  54	  55	  47	  53	  60	  56	  61
-partition: 0059	total: 556	per_label:   50	  56	  56	  54	  59	  62	  45	  59	  59	  56
-partition: 0060	total: 568	per_label:   69	  55	  62	  62	  50	  36	  57	  59	  54	  64
-partition: 0061	total: 579	per_label:   64	  58	  48	  50	  62	  62	  63	  62	  55	  55
-partition: 0062	total: 590	per_label:   58	  64	  60	  55	  53	  54	  69	  68	  44	  65
-partition: 0063	total: 602	per_label:   64	  50	  61	  71	  59	  49	  58	  56	  61	  73
-partition: 0064	total: 614	per_label:   71	  57	  58	  61	  58	  59	  62	  69	  53	  66
-partition: 0065	total: 627	per_label:   56	  62	  66	  74	  58	  55	  56	  54	  74	  72
-partition: 0066	total: 639	per_label:   61	  71	  55	  60	  48	  64	  67	  59	  81	  73
-partition: 0067	total: 652	per_label:   72	  71	  75	  67	  64	  57	  61	  54	  53	  78
-partition: 0068	total: 665	per_label:   67	  87	  70	  68	  78	  65	  61	  54	  59	  56
-partition: 0069	total: 678	per_label:   76	  72	  60	  56	  68	  66	  64	  72	  77	  67
-partition: 0070	total: 692	per_label:   64	  63	  63	  74	  60	  71	  78	  76	  73	  70
-partition: 0071	total: 706	per_label:   92	  64	  58	  59	  84	  65	  68	  74	  72	  70
-partition: 0072	total: 720	per_label:   70	  85	  67	  75	  70	  79	  88	  70	  65	  51
-partition: 0073	total: 734	per_label:   80	  60	  70	  73	  78	  77	  71	  78	  80	  67
-partition: 0074	total: 749	per_label:   60	  74	  85	  69	  82	  73	  76	  75	  94	  61
-partition: 0075	total: 764	per_label:   75	  76	  80	  74	  68	  82	  77	  75	  80	  77
-partition: 0076	total: 779	per_label:   84	  83	  76	  72	  75	  79	  84	  81	  74	  71
-partition: 0077	total: 795	per_label:   72	  67	  83	  90	  94	  83	  92	  82	  72	  60
-partition: 0078	total: 811	per_label:   80	  81	  94	  74	  88	  88	  66	  72	  68	 100
-partition: 0079	total: 827	per_label:   84	  74	  83	  89	  83	  83	  83	  87	  83	  78
-partition: 0080	total: 844	per_label:   88	  80	  97	  81	  89	  75	  85	  84	  81	  84
-partition: 0081	total: 860	per_label:   78	  83	  77	  73	  90	  98	  98	  72	 103	  88
-partition: 0082	total: 878	per_label:   90	  95	  81	  83	 106	  82	  93	  88	  75	  85
-partition: 0083	total: 895	per_label:   92	  81	  99	  99	  89	  94	  82	  91	  81	  87
-partition: 0084	total: 913	per_label:   86	  74	  83	  96	  93	 102	  90	  85	 105	  99
-partition: 0085	total: 931	per_label:  104	 105	  85	 105	  87	  88	  82	  88	 104	  83
-partition: 0086	total: 950	per_label:   95	 101	  95	 103	  96	  85	  92	 103	  92	  88
-partition: 0087	total: 969	per_label:   90	  93	  89	 110	  80	 102	 110	 103	  88	 104
-partition: 0088	total: 988	per_label:   95	 110	 126	  93	  90	  85	  90	  95	 113	  91
-partition: 0089	total: 1008	per_label:  108	  98	  85	  86	 115	  93	 108	  97	 102	 116
-partition: 0090	total: 1028	per_label:   98	 104	 116	  98	 105	  98	  98	 107	  92	 112
-partition: 0091	total: 1049	per_label:  114	  96	 122	 107	  99	 113	 102	  97	 100	  99
-partition: 0092	total: 1070	per_label:  106	 116	  99	 112	 103	  94	  98	 119	 108	 115
-partition: 0093	total: 1091	per_label:   97	  90	 132	 118	 125	 119	  93	 102	 104	 111
-partition: 0094	total: 1113	per_label:  118	 123	 105	  96	 109	 131	 106	  93	 125	 107
-partition: 0095	total: 1135	per_label:  119	 100	 101	 114	 116	 138	 110	 111	 120	 106
-partition: 0096	total: 1158	per_label:  113	 116	 107	 124	 129	 103	 117	 126	 120	 103
-partition: 0097	total: 1181	per_label:  121	 116	 126	 119	 111	 143	 108	 130	  89	 118
-partition: 0098	total: 1205	per_label:   98	 137	 115	 112	 118	 128	 131	 111	 137	 118
-partition: 0099	total: 1229	per_label:  127	 124	 132	 118	 114	 113	 134	 113	 131	 123
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_025.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_025.txt
deleted file mode 100644
index 93d21ab3b..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_025.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_025
-number_of_examples_per_label_per_shard
-partition: 0000	total: 124	per_label:   14	  14	   4	  12	  12	  13	  19	  15	   9	  12
-partition: 0001	total: 128	per_label:   13	  13	  15	  12	  16	  12	  10	  13	  11	  13
-partition: 0002	total: 131	per_label:   13	  12	  13	  13	  16	  13	  12	  16	  16	   7
-partition: 0003	total: 134	per_label:   11	  13	  11	  15	  15	  20	  15	  15	  15	   4
-partition: 0004	total: 137	per_label:   12	  14	  16	  13	  15	  12	  10	   9	  20	  16
-partition: 0005	total: 141	per_label:   16	  17	  13	  12	  14	  16	  12	  15	  11	  15
-partition: 0006	total: 144	per_label:   11	  12	  12	  16	  14	  16	  18	  17	  10	  18
-partition: 0007	total: 148	per_label:   12	  14	  16	  10	  19	  14	   9	  17	  21	  16
-partition: 0008	total: 152	per_label:   17	  11	  16	  18	   9	  10	  31	  20	   7	  13
-partition: 0009	total: 156	per_label:   20	  21	  14	  12	  20	  13	  15	  12	  14	  15
-partition: 0010	total: 159	per_label:   19	  19	  11	  15	  23	  20	  11	  14	  16	  11
-partition: 0011	total: 163	per_label:   18	  15	  14	  11	  19	   9	  21	  14	  16	  26
-partition: 0012	total: 168	per_label:   20	  21	  15	  14	  22	  18	  17	  15	  16	  10
-partition: 0013	total: 172	per_label:   14	  23	  15	  20	   7	  27	  14	  21	  15	  16
-partition: 0014	total: 176	per_label:   15	  16	  11	  21	  20	  16	  16	  23	  21	  17
-partition: 0015	total: 180	per_label:   23	  25	  13	  19	  14	  27	  16	  16	  19	   8
-partition: 0016	total: 185	per_label:   19	  18	  24	  18	  17	  15	  20	  21	  14	  19
-partition: 0017	total: 190	per_label:   19	  16	  15	  18	  18	  18	  22	  17	  22	  25
-partition: 0018	total: 194	per_label:   18	  23	  18	  13	  24	  12	  19	  23	  24	  20
-partition: 0019	total: 199	per_label:   19	  22	  17	  18	  14	  28	  26	  21	  10	  24
-partition: 0020	total: 204	per_label:   21	  28	  21	  15	  19	  19	  21	  18	  23	  19
-partition: 0021	total: 209	per_label:   24	  19	  15	  25	  23	  23	  22	  24	  18	  16
-partition: 0022	total: 215	per_label:   25	  18	  19	  22	  19	  18	  24	  20	  20	  30
-partition: 0023	total: 220	per_label:   23	  22	  20	  23	  18	  29	  26	  25	  18	  16
-partition: 0024	total: 226	per_label:   23	  24	  28	  22	  27	  17	  24	  23	  16	  22
-partition: 0025	total: 231	per_label:   28	  18	  33	  23	  20	  19	  21	  22	  25	  22
-partition: 0026	total: 237	per_label:   12	  23	  22	  24	  23	  20	  37	  20	  28	  28
-partition: 0027	total: 243	per_label:   24	  25	  25	  28	  26	  18	  21	  24	  26	  26
-partition: 0028	total: 249	per_label:   29	  21	  28	  26	  32	  26	  24	  25	  16	  22
-partition: 0029	total: 255	per_label:   29	  30	  28	  33	  15	  29	  25	  27	  21	  18
-partition: 0030	total: 262	per_label:   25	  22	  25	  29	  31	  28	  20	  21	  35	  26
-partition: 0031	total: 268	per_label:   23	  22	  34	  32	  29	  21	  27	  27	  26	  27
-partition: 0032	total: 275	per_label:   33	  39	  30	  26	  23	  30	  27	  17	  29	  21
-partition: 0033	total: 282	per_label:   28	  23	  34	  24	  22	  24	  32	  31	  37	  27
-partition: 0034	total: 289	per_label:   28	  26	  29	  30	  26	  32	  33	  23	  36	  26
-partition: 0035	total: 296	per_label:   32	  28	  34	  34	  33	  28	  20	  28	  33	  26
-partition: 0036	total: 303	per_label:   31	  45	  22	  29	  30	  32	  20	  38	  21	  35
-partition: 0037	total: 311	per_label:   30	  29	  21	  34	  27	  26	  40	  30	  37	  37
-partition: 0038	total: 319	per_label:   26	  41	  20	  31	  24	  35	  39	  45	  30	  28
-partition: 0039	total: 327	per_label:   31	  33	  43	  25	  36	  37	  23	  29	  26	  44
-partition: 0040	total: 335	per_label:   24	  32	  35	  34	  35	  44	  23	  30	  35	  43
-partition: 0041	total: 343	per_label:   36	  28	  33	  30	  36	  35	  38	  40	  31	  36
-partition: 0042	total: 352	per_label:   27	  29	  31	  35	  37	  46	  34	  35	  44	  34
-partition: 0043	total: 361	per_label:   37	  37	  39	  34	  39	  45	  27	  38	  38	  27
-partition: 0044	total: 370	per_label:   34	  33	  38	  37	  33	  40	  48	  36	  36	  35
-partition: 0045	total: 379	per_label:   42	  39	  29	  34	  37	  41	  35	  44	  33	  45
-partition: 0046	total: 389	per_label:   42	  37	  46	  42	  35	  43	  37	  29	  40	  38
-partition: 0047	total: 398	per_label:   41	  32	  41	  50	  45	  43	  36	  34	  40	  36
-partition: 0048	total: 408	per_label:   37	  38	  41	  41	  48	  46	  35	  46	  34	  42
-partition: 0049	total: 419	per_label:   37	  43	  40	  44	  45	  40	  39	  50	  31	  50
-partition: 0050	total: 429	per_label:   46	  47	  34	  43	  46	  34	  50	  46	  44	  39
-partition: 0051	total: 440	per_label:   54	  47	  47	  41	  41	  35	  50	  42	  45	  38
-partition: 0052	total: 451	per_label:   55	  44	  56	  51	  32	  39	  48	  45	  51	  30
-partition: 0053	total: 462	per_label:   47	  41	  49	  58	  42	  41	  47	  46	  48	  43
-partition: 0054	total: 474	per_label:   35	  56	  59	  39	  34	  45	  50	  56	  44	  56
-partition: 0055	total: 485	per_label:   44	  51	  50	  37	  51	  58	  42	  40	  46	  66
-partition: 0056	total: 498	per_label:   53	  43	  66	  50	  51	  49	  54	  42	  47	  43
-partition: 0057	total: 510	per_label:   38	  56	  48	  51	  46	  56	  46	  54	  59	  56
-partition: 0058	total: 523	per_label:   52	  51	  35	  54	  49	  55	  51	  59	  57	  60
-partition: 0059	total: 536	per_label:   59	  51	  61	  58	  40	  38	  51	  52	  61	  65
-partition: 0060	total: 549	per_label:   41	  43	  55	  64	  63	  46	  56	  57	  59	  65
-partition: 0061	total: 563	per_label:   50	  54	  54	  59	  61	  58	  61	  66	  52	  48
-partition: 0062	total: 577	per_label:   59	  68	  59	  58	  67	  46	  59	  55	  44	  62
-partition: 0063	total: 592	per_label:   67	  61	  46	  57	  61	  58	  57	  63	  60	  62
-partition: 0064	total: 606	per_label:   60	  62	  63	  59	  61	  58	  51	  65	  63	  64
-partition: 0065	total: 622	per_label:   74	  57	  66	  62	  63	  42	  71	  61	  64	  62
-partition: 0066	total: 637	per_label:   66	  71	  58	  56	  62	  69	  62	  73	  54	  66
-partition: 0067	total: 653	per_label:   64	  62	  55	  71	  58	  57	  73	  74	  57	  82
-partition: 0068	total: 669	per_label:   71	  64	  68	  70	  67	  61	  64	  64	  59	  81
-partition: 0069	total: 686	per_label:   68	  61	  75	  79	  64	  68	  60	  69	  73	  69
-partition: 0070	total: 703	per_label:   63	  76	  58	  72	  56	  61	  76	  64	  91	  86
-partition: 0071	total: 721	per_label:   85	  76	  84	  73	  68	  64	  72	  57	  63	  79
-partition: 0072	total: 739	per_label:   73	  95	  80	  71	  85	  73	  68	  66	  62	  66
-partition: 0073	total: 757	per_label:   83	  79	  65	  66	  72	  76	  76	  77	  89	  74
-partition: 0074	total: 776	per_label:   80	  70	  69	  77	  80	  72	  84	  89	  79	  76
-partition: 0075	total: 796	per_label:   97	  83	  68	  73	  84	  80	  82	  76	  78	  75
-partition: 0076	total: 816	per_label:   88	  82	  77	  83	  80	 100	  85	  83	  71	  67
-partition: 0077	total: 836	per_label:   69	  72	  92	  78	 103	  68	  85	  83	 115	  71
-partition: 0078	total: 857	per_label:   78	  88	  95	  89	  72	  93	  88	  85	  89	  80
-partition: 0079	total: 878	per_label:   98	  95	  82	  76	  82	  89	  97	  94	  83	  82
-partition: 0080	total: 900	per_label:   80	  77	  92	 100	 108	 100	  99	  93	  85	  66
-partition: 0081	total: 923	per_label:   88	  94	 104	  84	  98	  98	  83	  83	  78	 113
-partition: 0082	total: 946	per_label:  100	  83	 101	 100	 102	  83	  95	  92	 101	  89
-partition: 0083	total: 970	per_label:   96	  91	 114	  87	  93	  97	  97	  97	  98	 100
-partition: 0084	total: 994	per_label:   93	 100	  77	  99	 122	 111	 104	  82	 106	 100
-partition: 0085	total: 1019	per_label:  109	 104	 105	  97	 109	  96	  99	 114	  86	 100
-partition: 0086	total: 1044	per_label:   99	  84	 104	 116	  98	 117	 108	  96	 116	 106
-partition: 0087	total: 1070	per_label:  117	 118	  97	 119	 103	 105	  93	 106	 114	  98
-partition: 0088	total: 1097	per_label:  106	 124	 105	 124	 105	  96	 114	 114	 106	 103
-partition: 0089	total: 1125	per_label:  110	 103	 121	 115	  96	 118	 123	 110	 112	 117
-partition: 0090	total: 1153	per_label:  119	 118	 120	 113	 118	  93	 105	 117	 121	 129
-partition: 0091	total: 1182	per_label:  116	 120	 117	 106	 134	 123	 122	 106	 115	 123
-partition: 0092	total: 1211	per_label:  125	 111	 152	 123	 109	 122	 111	 131	 109	 118
-partition: 0093	total: 1241	per_label:  127	 137	 108	 124	 121	 113	 119	 134	 127	 131
-partition: 0094	total: 1272	per_label:  111	 112	 156	 134	 141	 138	 108	 117	 124	 131
-partition: 0095	total: 1304	per_label:  142	 134	 119	 112	 123	 162	 133	 116	 149	 114
-partition: 0096	total: 1337	per_label:  151	 122	 122	 143	 143	 136	 126	 126	 138	 130
-partition: 0097	total: 1370	per_label:  122	 138	 137	 140	 150	 146	 124	 163	 122	 128
-partition: 0098	total: 1405	per_label:  120	 158	 136	 131	 131	 153	 149	 137	 147	 143
-partition: 0099	total: 1440	per_label:  147	 143	 152	 142	 134	 141	 161	 130	 149	 141
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_030.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_030.txt
deleted file mode 100644
index c201dd9ed..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_030.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_030
-number_of_examples_per_label_per_shard
-partition: 0000	total: 89	per_label:   12	  11	   3	  11	  11	   8	  10	   7	   5	  11
-partition: 0001	total: 91	per_label:    5	  11	   8	   6	  10	  11	  13	  12	   9	   6
-partition: 0002	total: 94	per_label:   10	   8	   9	  11	   9	   8	   8	  12	   9	  10
-partition: 0003	total: 97	per_label:   10	   9	  11	   9	  12	  11	   8	  13	  11	   3
-partition: 0004	total: 100	per_label:   11	   9	   7	  11	  13	  14	  12	   8	  12	   3
-partition: 0005	total: 103	per_label:    7	  13	  13	  10	  10	  11	   9	   9	  14	   7
-partition: 0006	total: 106	per_label:   12	   9	  10	   7	  10	  10	   9	   9	  15	  15
-partition: 0007	total: 109	per_label:   12	  13	  11	  12	  12	  13	   9	  10	   6	  11
-partition: 0008	total: 112	per_label:    6	   9	   8	  11	   8	  15	  14	  19	   8	  14
-partition: 0009	total: 116	per_label:   10	  10	  12	  11	  16	   8	   9	  10	  18	  12
-partition: 0010	total: 119	per_label:   15	  10	  11	   8	  12	  11	  12	  18	   7	  15
-partition: 0011	total: 123	per_label:   12	  12	  14	  15	  11	   8	  25	  11	   7	   8
-partition: 0012	total: 126	per_label:   15	  16	  13	   9	  15	   9	  13	  11	  12	  13
-partition: 0013	total: 130	per_label:   19	  14	   8	  12	  19	  15	   9	  11	  15	   8
-partition: 0014	total: 134	per_label:   14	  14	  11	  10	  14	  14	  13	  10	  11	  23
-partition: 0015	total: 138	per_label:   19	  15	  15	   9	  19	   7	  18	  11	  15	  10
-partition: 0016	total: 142	per_label:   10	  22	  12	  17	  14	  19	  12	  15	  11	  10
-partition: 0017	total: 147	per_label:   12	  15	  10	  16	   7	  24	  13	  21	  15	  14
-partition: 0018	total: 151	per_label:   14	  13	  10	  18	  18	  13	  14	  19	  16	  16
-partition: 0019	total: 156	per_label:   21	  26	  10	  15	  10	  22	  14	  13	  18	   7
-partition: 0020	total: 160	per_label:   13	  12	  22	  18	  16	  15	  17	  19	  15	  13
-partition: 0021	total: 165	per_label:   16	  18	  13	  16	  17	  13	  20	  15	  16	  21
-partition: 0022	total: 170	per_label:   18	  19	  14	  14	  16	  15	  16	  19	  20	  19
-partition: 0023	total: 175	per_label:   20	  18	  14	  13	  20	  13	  23	  15	  17	  22
-partition: 0024	total: 180	per_label:   18	  22	  17	  16	  15	  25	  20	  18	  12	  17
-partition: 0025	total: 186	per_label:   19	  23	  21	  13	  17	  21	  16	  22	  18	  16
-partition: 0026	total: 191	per_label:   20	  16	  16	  25	  20	  19	  20	  20	  16	  19
-partition: 0027	total: 197	per_label:   23	  17	  15	  19	  18	  17	  23	  19	  20	  26
-partition: 0028	total: 203	per_label:   20	  21	  19	  20	  17	  27	  23	  23	  18	  15
-partition: 0029	total: 209	per_label:   19	  22	  28	  19	  21	  19	  25	  20	  14	  22
-partition: 0030	total: 216	per_label:   30	  17	  27	  25	  25	  13	  17	  21	  22	  19
-partition: 0031	total: 222	per_label:   13	  21	  22	  20	  22	  21	  35	  21	  25	  22
-partition: 0032	total: 229	per_label:   23	  21	  24	  23	  20	  18	  23	  22	  25	  30
-partition: 0033	total: 236	per_label:   18	  25	  25	  29	  31	  20	  21	  23	  22	  22
-partition: 0034	total: 243	per_label:   31	  24	  29	  29	  20	  27	  23	  26	  15	  19
-partition: 0035	total: 250	per_label:   26	  26	  24	  29	  29	  27	  23	  22	  27	  17
-partition: 0036	total: 257	per_label:   31	  19	  28	  33	  20	  25	  27	  22	  26	  26
-partition: 0037	total: 265	per_label:   22	  30	  32	  25	  25	  23	  24	  22	  34	  28
-partition: 0038	total: 273	per_label:   27	  32	  30	  28	  22	  27	  27	  25	  36	  19
-partition: 0039	total: 281	per_label:   26	  21	  28	  25	  32	  22	  34	  29	  30	  34
-partition: 0040	total: 290	per_label:   34	  25	  29	  27	  25	  40	  28	  23	  38	  21
-partition: 0041	total: 299	per_label:   28	  46	  36	  35	  27	  26	  14	  27	  27	  33
-partition: 0042	total: 308	per_label:   37	  33	  22	  29	  28	  26	  31	  39	  27	  36
-partition: 0043	total: 317	per_label:   25	  34	  17	  31	  31	  32	  46	  36	  35	  30
-partition: 0044	total: 326	per_label:   30	  35	  33	  35	  28	  35	  27	  43	  27	  33
-partition: 0045	total: 336	per_label:   27	  38	  37	  24	  38	  41	  30	  26	  23	  52
-partition: 0046	total: 346	per_label:   26	  30	  38	  39	  32	  45	  26	  34	  42	  34
-partition: 0047	total: 357	per_label:   41	  28	  33	  29	  34	  40	  35	  41	  42	  34
-partition: 0048	total: 367	per_label:   29	  32	  39	  43	  50	  47	  27	  28	  38	  34
-partition: 0049	total: 378	per_label:   38	  34	  33	  29	  33	  46	  45	  49	  37	  34
-partition: 0050	total: 390	per_label:   37	  39	  37	  38	  33	  45	  45	  46	  34	  36
-partition: 0051	total: 401	per_label:   45	  43	  40	  43	  39	  39	  34	  33	  36	  49
-partition: 0052	total: 413	per_label:   40	  33	  47	  52	  43	  48	  36	  32	  45	  37
-partition: 0053	total: 426	per_label:   40	  36	  42	  42	  56	  46	  38	  42	  40	  44
-partition: 0054	total: 439	per_label:   44	  51	  42	  40	  47	  44	  35	  56	  33	  47
-partition: 0055	total: 452	per_label:   46	  42	  39	  51	  46	  33	  56	  49	  44	  46
-partition: 0056	total: 465	per_label:   57	  52	  46	  43	  43	  39	  53	  42	  49	  41
-partition: 0057	total: 479	per_label:   57	  47	  60	  55	  34	  42	  52	  50	  51	  31
-partition: 0058	total: 494	per_label:   51	  44	  51	  60	  44	  44	  50	  47	  52	  51
-partition: 0059	total: 509	per_label:   39	  63	  66	  38	  40	  47	  53	  56	  47	  60
-partition: 0060	total: 524	per_label:   55	  48	  48	  48	  57	  63	  52	  44	  49	  60
-partition: 0061	total: 540	per_label:   50	  56	  74	  52	  53	  54	  46	  49	  55	  51
-partition: 0062	total: 556	per_label:   43	  56	  53	  57	  43	  58	  64	  58	  54	  70
-partition: 0063	total: 573	per_label:   56	  52	  46	  59	  57	  53	  47	  63	  74	  66
-partition: 0064	total: 590	per_label:   60	  52	  58	  63	  52	  53	  63	  65	  64	  60
-partition: 0065	total: 607	per_label:   46	  53	  64	  72	  66	  51	  64	  57	  64	  70
-partition: 0066	total: 626	per_label:   59	  74	  62	  58	  74	  53	  63	  73	  50	  60
-partition: 0067	total: 644	per_label:   74	  68	  50	  64	  69	  59	  65	  68	  58	  69
-partition: 0068	total: 664	per_label:   69	  67	  65	  64	  68	  72	  53	  67	  69	  70
-partition: 0069	total: 684	per_label:   80	  65	  74	  74	  68	  42	  78	  68	  70	  65
-partition: 0070	total: 704	per_label:   74	  74	  65	  58	  69	  75	  69	  85	  58	  77
-partition: 0071	total: 725	per_label:   71	  66	  67	  87	  70	  63	  80	  71	  62	  88
-partition: 0072	total: 747	per_label:   83	  69	  76	  73	  68	  70	  75	  83	  69	  81
-partition: 0073	total: 770	per_label:   74	  77	  80	  88	  64	  71	  71	  67	  90	  88
-partition: 0074	total: 793	per_label:   82	  85	  73	  79	  70	  67	  84	  71	  93	  89
-partition: 0075	total: 816	per_label:   81	 102	  90	  80	  89	  82	  76	  66	  63	  87
-partition: 0076	total: 841	per_label:   92	  95	  78	  78	  90	  83	  73	  85	  89	  78
-partition: 0077	total: 866	per_label:   86	  81	  79	  82	  77	  84	  95	  98	  96	  88
-partition: 0078	total: 892	per_label:  108	  85	  76	  81	 100	  89	  98	  87	  89	  79
-partition: 0079	total: 919	per_label:   98	  90	  86	  96	  93	 107	  96	  95	  82	  76
-partition: 0080	total: 947	per_label:   76	  90	 106	  90	 107	  86	  91	  96	 126	  79
-partition: 0081	total: 975	per_label:   92	 100	 102	  95	  86	 103	 103	 101	  98	  95
-partition: 0082	total: 1004	per_label:  106	 102	 102	 100	  95	 107	 114	 102	  94	  82
-partition: 0083	total: 1034	per_label:   98	  85	 116	 109	 124	 111	  99	  89	  87	 116
-partition: 0084	total: 1065	per_label:  112	 101	 105	 100	 116	 104	 107	 111	 107	 102
-partition: 0085	total: 1097	per_label:  109	 108	 130	 100	 111	 101	 104	 107	 115	 112
-partition: 0086	total: 1130	per_label:  100	 114	  89	 112	 135	 128	 122	  99	 120	 111
-partition: 0087	total: 1164	per_label:  127	 114	 126	 120	 115	 113	 117	 124	  95	 113
-partition: 0088	total: 1199	per_label:  116	 112	 112	 126	 122	 124	 116	 108	 142	 121
-partition: 0089	total: 1235	per_label:  136	 130	 119	 138	 120	 113	 111	 124	 128	 116
-partition: 0090	total: 1272	per_label:  115	 126	 114	 142	 112	 135	 140	 140	 115	 133
-partition: 0091	total: 1311	per_label:  137	 135	 159	 127	 125	 111	 118	 123	 148	 128
-partition: 0092	total: 1350	per_label:  136	 139	 128	 120	 151	 132	 145	 125	 122	 152
-partition: 0093	total: 1390	per_label:  140	 132	 163	 139	 128	 144	 133	 147	 132	 132
-partition: 0094	total: 1432	per_label:  151	 135	 139	 147	 144	 132	 130	 153	 147	 154
-partition: 0095	total: 1475	per_label:  133	 146	 158	 153	 155	 170	 131	 134	 148	 147
-partition: 0096	total: 1519	per_label:  162	 151	 144	 139	 156	 172	 147	 139	 166	 143
-partition: 0097	total: 1565	per_label:  153	 158	 152	 169	 164	 153	 149	 176	 155	 136
-partition: 0098	total: 1612	per_label:  143	 168	 164	 153	 155	 190	 154	 155	 158	 172
-partition: 0099	total: 1660	per_label:  167	 171	 167	 158	 158	 159	 195	 156	 170	 159
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_035.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_035.txt
deleted file mode 100644
index 29778c4b7..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_035.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_035
-number_of_examples_per_label_per_shard
-partition: 0000	total: 62	per_label:   10	   6	   2	   8	   8	   6	   9	   6	   1	   6
-partition: 0001	total: 64	per_label:    4	   8	   2	   4	   4	   8	  10	  10	   8	   6
-partition: 0002	total: 67	per_label:    5	   9	   8	   7	  11	   5	   5	   5	   7	   5
-partition: 0003	total: 69	per_label:    8	   5	   7	   7	   5	   7	   6	   9	   5	  10
-partition: 0004	total: 71	per_label:    8	   9	  10	   7	   8	   6	   6	   7	   8	   2
-partition: 0005	total: 74	per_label:    5	   4	   5	   9	  11	  10	   6	  10	  11	   3
-partition: 0006	total: 77	per_label:   10	   7	   7	   6	  10	  11	  10	   7	   7	   2
-partition: 0007	total: 79	per_label:    4	  10	   8	   8	   7	   9	   8	   7	  12	   6
-partition: 0008	total: 82	per_label:    8	   7	   9	   7	   8	   8	   4	   7	  12	  12
-partition: 0009	total: 85	per_label:   12	  11	   9	  10	   7	   9	   9	   4	   8	   6
-partition: 0010	total: 88	per_label:    6	   9	   6	   8	  10	  12	   8	  14	   4	  11
-partition: 0011	total: 91	per_label:    6	   8	   7	   7	  11	  10	  11	  13	   6	  12
-partition: 0012	total: 94	per_label:    6	   6	  12	  11	  10	   7	   8	   8	  15	  11
-partition: 0013	total: 97	per_label:   13	  10	  10	   5	  11	   9	   6	  12	   9	  12
-partition: 0014	total: 101	per_label:   14	   6	   9	  10	   8	   6	  23	  14	   5	   6
-partition: 0015	total: 104	per_label:   10	  15	  11	  11	  11	   8	  14	   7	   6	  11
-partition: 0016	total: 108	per_label:   12	  16	  12	   9	  11	  11	   9	   9	  11	   8
-partition: 0017	total: 112	per_label:   15	  10	   4	   9	  19	  13	   9	  12	  14	   7
-partition: 0018	total: 116	per_label:   13	  12	   8	   9	  12	  10	  12	   9	  10	  21
-partition: 0019	total: 120	per_label:   13	  11	  15	  10	  14	   8	  17	   9	  11	  12
-partition: 0020	total: 124	per_label:   14	  17	   9	  11	  18	  13	  10	  13	  12	   7
-partition: 0021	total: 129	per_label:   12	  16	  12	  15	   6	  20	  12	  15	  12	   9
-partition: 0022	total: 133	per_label:   11	  11	   9	  16	  12	  17	   9	  22	  10	  16
-partition: 0023	total: 138	per_label:   15	  20	  10	  15	  12	  11	  12	  10	  23	  10
-partition: 0024	total: 143	per_label:   16	  18	  10	  15	  11	  22	  14	  16	  14	   7
-partition: 0025	total: 148	per_label:   15	  14	  21	  15	  14	  12	  17	  14	   9	  17
-partition: 0026	total: 153	per_label:   13	  15	  11	  14	  16	  14	  17	  17	  17	  19
-partition: 0027	total: 158	per_label:   18	  18	  14	  13	  14	  12	  17	  17	  20	  15
-partition: 0028	total: 164	per_label:   16	  18	  12	  13	  20	  13	  21	  14	  15	  22
-partition: 0029	total: 169	per_label:   17	  19	  17	  15	  14	  22	  21	  18	   9	  17
-partition: 0030	total: 175	per_label:   17	  23	  20	  12	  16	  20	  14	  18	  19	  16
-partition: 0031	total: 182	per_label:   21	  17	  10	  21	  20	  21	  20	  22	  14	  16
-partition: 0032	total: 188	per_label:   21	  18	  19	  18	  19	  16	  18	  15	  19	  25
-partition: 0033	total: 194	per_label:   19	  17	  19	  23	  18	  22	  21	  23	  18	  14
-partition: 0034	total: 201	per_label:   16	  20	  24	  19	  16	  23	  24	  20	  16	  23
-partition: 0035	total: 208	per_label:   29	  22	  26	  21	  23	  10	  22	  17	  19	  19
-partition: 0036	total: 216	per_label:   22	  14	  26	  20	  21	  18	  29	  28	  23	  15
-partition: 0037	total: 223	per_label:   17	  25	  18	  24	  15	  20	  25	  20	  28	  31
-partition: 0038	total: 231	per_label:   16	  23	  30	  30	  29	  18	  21	  21	  21	  22
-partition: 0039	total: 239	per_label:   30	  23	  22	  23	  30	  30	  23	  23	  13	  22
-partition: 0040	total: 248	per_label:   27	  26	  26	  31	  18	  27	  28	  27	  23	  15
-partition: 0041	total: 256	per_label:   27	  19	  25	  32	  26	  23	  24	  19	  35	  26
-partition: 0042	total: 265	per_label:   20	  24	  37	  28	  30	  23	  22	  25	  27	  29
-partition: 0043	total: 275	per_label:   35	  37	  28	  27	  21	  29	  29	  20	  28	  21
-partition: 0044	total: 284	per_label:   27	  23	  32	  23	  30	  21	  32	  33	  35	  28
-partition: 0045	total: 294	per_label:   32	  27	  27	  31	  23	  38	  31	  23	  38	  24
-partition: 0046	total: 304	per_label:   28	  38	  39	  34	  30	  27	  16	  27	  30	  35
-partition: 0047	total: 315	per_label:   35	  42	  22	  29	  33	  30	  26	  39	  27	  32
-partition: 0048	total: 326	per_label:   27	  30	  20	  36	  25	  32	  46	  35	  39	  36
-partition: 0049	total: 338	per_label:   37	  38	  27	  33	  33	  35	  32	  44	  29	  30
-partition: 0050	total: 349	per_label:   25	  36	  45	  24	  39	  42	  32	  29	  22	  55
-partition: 0051	total: 362	per_label:   27	  35	  36	  43	  33	  46	  25	  35	  45	  37
-partition: 0052	total: 374	per_label:   42	  29	  35	  30	  35	  42	  38	  43	  44	  36
-partition: 0053	total: 387	per_label:   31	  35	  40	  44	  52	  50	  28	  32	  40	  35
-partition: 0054	total: 401	per_label:   39	  37	  39	  33	  33	  48	  51	  48	  37	  36
-partition: 0055	total: 415	per_label:   44	  41	  36	  40	  39	  47	  42	  48	  39	  39
-partition: 0056	total: 430	per_label:   46	  45	  49	  42	  41	  43	  38	  32	  42	  52
-partition: 0057	total: 445	per_label:   47	  37	  44	  57	  49	  48	  40	  37	  44	  42
-partition: 0058	total: 460	per_label:   39	  43	  47	  47	  56	  49	  40	  53	  38	  48
-partition: 0059	total: 476	per_label:   43	  50	  43	  51	  46	  47	  46	  55	  40	  55
-partition: 0060	total: 493	per_label:   61	  57	  43	  41	  52	  36	  60	  48	  48	  47
-partition: 0061	total: 510	per_label:   58	  49	  62	  53	  39	  43	  58	  51	  59	  38
-partition: 0062	total: 528	per_label:   60	  49	  56	  70	  45	  47	  50	  56	  58	  37
-partition: 0063	total: 547	per_label:   43	  61	  65	  50	  46	  53	  63	  57	  52	  57
-partition: 0064	total: 566	per_label:   52	  57	  62	  45	  57	  63	  50	  50	  51	  79
-partition: 0065	total: 586	per_label:   62	  54	  76	  57	  62	  59	  55	  51	  57	  53
-partition: 0066	total: 606	per_label:   48	  64	  55	  61	  46	  64	  66	  66	  62	  74
-partition: 0067	total: 628	per_label:   69	  58	  52	  68	  58	  56	  53	  62	  82	  70
-partition: 0068	total: 650	per_label:   53	  54	  72	  67	  66	  59	  69	  74	  62	  74
-partition: 0069	total: 672	per_label:   57	  63	  61	  79	  74	  62	  68	  75	  68	  65
-partition: 0070	total: 696	per_label:   74	  83	  69	  69	  74	  58	  75	  63	  58	  73
-partition: 0071	total: 720	per_label:   74	  72	  59	  70	  78	  73	  63	  81	  74	  76
-partition: 0072	total: 745	per_label:   81	  73	  81	  76	  73	  53	  75	  78	  75	  80
-partition: 0073	total: 772	per_label:   85	  78	  70	  66	  76	  81	  82	  86	  74	  74
-partition: 0074	total: 799	per_label:   83	  79	  71	  88	  73	  70	  85	  86	  64	 100
-partition: 0075	total: 827	per_label:   91	  76	  86	  84	  79	  74	  83	  87	  75	  92
-partition: 0076	total: 855	per_label:   82	  86	  90	  97	  75	  80	  80	  74	  95	  96
-partition: 0077	total: 885	per_label:   95	  96	  83	  85	  77	  73	  92	  78	 103	 103
-partition: 0078	total: 916	per_label:   87	 112	 103	  93	 107	  96	  80	  80	  73	  85
-partition: 0079	total: 949	per_label:  102	 101	  85	  87	  87	  91	  98	  96	 109	  93
-partition: 0080	total: 982	per_label:  111	  84	  83	  90	 113	 102	  93	 107	 100	  99
-partition: 0081	total: 1016	per_label:  111	 115	  90	  95	  94	 113	 120	 102	  90	  86
-partition: 0082	total: 1052	per_label:   94	  86	 116	 107	 126	  92	 102	 108	 135	  86
-partition: 0083	total: 1089	per_label:  102	 117	 116	 106	  94	 117	 112	 107	 115	 103
-partition: 0084	total: 1127	per_label:  117	 112	 112	 115	 107	 120	 127	 122	 103	  92
-partition: 0085	total: 1166	per_label:  111	 106	 128	 115	 140	 123	 107	 106	 102	 128
-partition: 0086	total: 1207	per_label:  122	 113	 126	 125	 128	 115	 124	 122	 120	 112
-partition: 0087	total: 1249	per_label:  116	 119	 128	 110	 127	 130	 135	 113	 142	 129
-partition: 0088	total: 1293	per_label:  137	 129	 121	 122	 156	 133	 119	 131	 117	 128
-partition: 0089	total: 1338	per_label:  131	 117	 136	 144	 126	 140	 142	 129	 135	 138
-partition: 0090	total: 1385	per_label:  146	 156	 123	 160	 136	 130	 122	 138	 150	 124
-partition: 0091	total: 1434	per_label:  137	 142	 139	 158	 130	 149	 152	 145	 134	 148
-partition: 0092	total: 1484	per_label:  153	 149	 173	 143	 139	 123	 145	 144	 163	 152
-partition: 0093	total: 1536	per_label:  151	 159	 153	 136	 169	 151	 154	 153	 142	 168
-partition: 0094	total: 1589	per_label:  162	 149	 174	 168	 153	 152	 157	 167	 156	 151
-partition: 0095	total: 1645	per_label:  156	 158	 180	 171	 169	 174	 142	 165	 155	 175
-partition: 0096	total: 1703	per_label:  177	 173	 163	 152	 167	 201	 164	 151	 196	 159
-partition: 0097	total: 1762	per_label:  183	 166	 155	 192	 201	 176	 170	 178	 174	 167
-partition: 0098	total: 1824	per_label:  167	 191	 196	 173	 173	 212	 172	 193	 166	 181
-partition: 0099	total: 1888	per_label:  184	 198	 190	 182	 176	 182	 213	 174	 203	 186
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_040.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_040.txt
deleted file mode 100644
index 16c1eb6f7..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_040.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_040
-number_of_examples_per_label_per_shard
-partition: 0000	total: 43	per_label:    6	   4	   1	   7	   5	   4	   6	   4	   1	   5
-partition: 0001	total: 45	per_label:    5	   7	   2	   4	   6	   4	   4	   3	   4	   6
-partition: 0002	total: 47	per_label:    3	   5	   2	   2	   3	   7	  10	  10	   4	   1
-partition: 0003	total: 49	per_label:    3	   6	   6	   5	   8	   4	   4	   2	   6	   5
-partition: 0004	total: 51	per_label:    6	   4	   7	   2	   5	   6	   4	   6	   3	   8
-partition: 0005	total: 53	per_label:    5	   7	   4	   9	   3	   2	   4	   9	   8	   2
-partition: 0006	total: 55	per_label:    7	   4	   8	   6	   8	   7	   4	   5	   4	   2
-partition: 0007	total: 57	per_label:    5	   3	   3	   5	   9	   7	   5	   8	   9	   3
-partition: 0008	total: 59	per_label:    7	   8	   3	   7	   7	   9	   7	   4	   6	   1
-partition: 0009	total: 62	per_label:    4	   5	   8	   6	   5	   8	   8	   9	   6	   3
-partition: 0010	total: 64	per_label:    8	   9	   7	   5	   6	   7	   6	   1	  10	   5
-partition: 0011	total: 67	per_label:    4	   4	   8	   7	   7	   5	   4	   7	  10	  11
-partition: 0012	total: 69	per_label:   10	   7	   7	   7	   6	   8	   7	   4	   8	   5
-partition: 0013	total: 72	per_label:    6	  10	   6	   5	  10	   8	   5	  10	   3	   9
-partition: 0014	total: 75	per_label:    5	   5	   5	   8	   4	  11	  10	  12	   5	  10
-partition: 0015	total: 78	per_label:    6	   8	   7	   8	  11	   5	   9	   7	   5	  12
-partition: 0016	total: 81	per_label:    7	   6	   8	   7	  10	   7	   4	  11	  15	   6
-partition: 0017	total: 85	per_label:   10	   9	  10	   5	   9	   9	   6	  10	   6	  11
-partition: 0018	total: 88	per_label:   11	   4	   9	   9	   7	   5	  22	  11	   5	   5
-partition: 0019	total: 92	per_label:   10	  13	   9	  11	  10	   6	  13	   6	   5	   9
-partition: 0020	total: 95	per_label:    9	  12	  10	   6	  10	   9	   9	  10	  11	   9
-partition: 0021	total: 99	per_label:   14	  12	   6	  11	  13	  13	   8	   6	   9	   7
-partition: 0022	total: 103	per_label:   11	  11	   6	   7	  16	   8	   8	  12	  12	  12
-partition: 0023	total: 107	per_label:   13	   9	  13	   8	   9	   8	  13	   8	   8	  18
-partition: 0024	total: 111	per_label:   16	  15	  10	   6	  16	   7	  13	  10	  13	   5
-partition: 0025	total: 116	per_label:    6	  16	  10	  16	  12	  18	  10	  11	   9	   8
-partition: 0026	total: 121	per_label:   13	  15	  10	  12	   6	  17	  11	  15	  11	  11
-partition: 0027	total: 125	per_label:   11	   7	   9	  15	  14	  15	   9	  18	  11	  16
-partition: 0028	total: 130	per_label:   16	  20	   8	  13	   9	  16	  10	  11	  20	   7
-partition: 0029	total: 136	per_label:   13	  18	   9	  17	  12	  16	  14	  16	  14	   7
-partition: 0030	total: 141	per_label:   15	  12	  21	  13	  13	  12	  17	  13	   9	  16
-partition: 0031	total: 147	per_label:   13	  14	  11	  13	  16	  13	  17	  16	  16	  18
-partition: 0032	total: 153	per_label:   15	  19	  12	  13	  14	  13	  15	  17	  19	  16
-partition: 0033	total: 159	per_label:   19	  13	  14	  11	  19	  13	  20	  15	  16	  19
-partition: 0034	total: 165	per_label:   15	  23	  15	  16	  12	  22	  20	  18	   7	  17
-partition: 0035	total: 172	per_label:   15	  22	  18	  10	  17	  19	  16	  17	  21	  17
-partition: 0036	total: 179	per_label:   19	  18	  11	  22	  20	  20	  20	  20	  12	  17
-partition: 0037	total: 186	per_label:   24	  14	  17	  20	  19	  14	  19	  17	  18	  24
-partition: 0038	total: 193	per_label:   19	  18	  24	  21	  18	  23	  19	  21	  18	  12
-partition: 0039	total: 201	per_label:   18	  19	  19	  19	  16	  20	  26	  21	  20	  23
-partition: 0040	total: 209	per_label:   27	  24	  25	  21	  23	  13	  20	  19	  16	  21
-partition: 0041	total: 218	per_label:   25	  16	  28	  21	  19	  21	  28	  24	  22	  14
-partition: 0042	total: 226	per_label:   15	  25	  19	  24	  18	  18	  28	  22	  28	  29
-partition: 0043	total: 235	per_label:   17	  23	  31	  30	  28	  20	  22	  17	  22	  25
-partition: 0044	total: 245	per_label:   30	  21	  23	  24	  31	  27	  23	  29	  15	  22
-partition: 0045	total: 255	per_label:   28	  28	  27	  31	  19	  29	  28	  24	  24	  17
-partition: 0046	total: 265	per_label:   28	  21	  25	  33	  26	  24	  24	  23	  35	  26
-partition: 0047	total: 275	per_label:   24	  23	  38	  28	  32	  23	  24	  25	  28	  30
-partition: 0048	total: 286	per_label:   33	  39	  32	  28	  20	  31	  29	  22	  31	  21
-partition: 0049	total: 298	per_label:   28	  24	  29	  28	  32	  21	  34	  32	  39	  31
-partition: 0050	total: 310	per_label:   33	  28	  31	  30	  27	  42	  32	  25	  38	  24
-partition: 0051	total: 322	per_label:   31	  46	  38	  35	  30	  30	  15	  27	  32	  38
-partition: 0052	total: 335	per_label:   38	  38	  23	  36	  29	  28	  32	  43	  29	  39
-partition: 0053	total: 349	per_label:   27	  35	  20	  34	  36	  36	  51	  42	  37	  31
-partition: 0054	total: 363	per_label:   35	  39	  43	  34	  34	  42	  28	  41	  28	  39
-partition: 0055	total: 377	per_label:   30	  39	  37	  31	  39	  51	  27	  32	  35	  56
-partition: 0056	total: 392	per_label:   38	  32	  42	  38	  39	  40	  41	  43	  38	  41
-partition: 0057	total: 408	per_label:   29	  34	  34	  45	  44	  55	  34	  40	  55	  38
-partition: 0058	total: 424	per_label:   45	  41	  42	  32	  45	  51	  43	  46	  43	  36
-partition: 0059	total: 441	per_label:   42	  41	  41	  49	  39	  50	  51	  54	  37	  37
-partition: 0060	total: 459	per_label:   53	  49	  47	  44	  44	  46	  41	  38	  40	  57
-partition: 0061	total: 477	per_label:   50	  39	  54	  59	  51	  52	  43	  35	  52	  42
-partition: 0062	total: 496	per_label:   43	  49	  50	  50	  59	  52	  43	  55	  41	  54
-partition: 0063	total: 516	per_label:   49	  51	  49	  54	  51	  48	  50	  61	  44	  59
-partition: 0064	total: 537	per_label:   65	  61	  46	  45	  56	  40	  66	  53	  54	  51
-partition: 0065	total: 558	per_label:   66	  53	  70	  62	  41	  51	  58	  55	  68	  34
-partition: 0066	total: 581	per_label:   61	  52	  59	  74	  54	  51	  62	  57	  54	  57
-partition: 0067	total: 604	per_label:   45	  69	  74	  46	  48	  63	  60	  68	  63	  68
-partition: 0068	total: 628	per_label:   66	  59	  73	  57	  67	  69	  63	  56	  54	  64
-partition: 0069	total: 653	per_label:   51	  72	  70	  68	  59	  69	  59	  60	  71	  74
-partition: 0070	total: 680	per_label:   69	  64	  53	  67	  64	  62	  68	  73	  79	  81
-partition: 0071	total: 707	per_label:   69	  62	  73	  77	  62	  64	  69	  77	  76	  78
-partition: 0072	total: 735	per_label:   58	  67	  75	  86	  80	  66	  76	  75	  72	  80
-partition: 0073	total: 765	per_label:   82	  89	  75	  75	  85	  64	  80	  75	  63	  77
-partition: 0074	total: 795	per_label:   82	  80	  67	  74	  89	  83	  71	  91	  77	  81
-partition: 0075	total: 827	per_label:   91	  82	  89	  85	  75	  55	  82	  89	  89	  90
-partition: 0076	total: 860	per_label:   94	  87	  76	  76	  88	  91	  94	  96	  71	  87
-partition: 0077	total: 894	per_label:   90	  81	  90	 100	  87	  82	  89	  86	  80	 109
-partition: 0078	total: 930	per_label:   93	  87	  96	 103	  86	  85	  86	  96	  92	 106
-partition: 0079	total: 968	per_label:   93	 105	  90	  96	  75	  86	 105	  90	 120	 108
-partition: 0080	total: 1006	per_label:  112	 122	 109	 101	 109	  92	  97	  79	  80	 105
-partition: 0081	total: 1046	per_label:  110	 114	  98	  95	 103	 110	  98	 105	 118	  95
-partition: 0082	total: 1088	per_label:  117	  99	  95	 103	 121	 107	 106	 117	 111	 112
-partition: 0083	total: 1132	per_label:  130	 123	  99	 108	 105	 123	 132	 113	 101	  98
-partition: 0084	total: 1177	per_label:  101	 104	 132	 120	 136	 104	 115	 123	 151	  91
-partition: 0085	total: 1224	per_label:  117	 130	 128	 113	 115	 131	 131	 126	 118	 115
-partition: 0086	total: 1273	per_label:  129	 108	 123	 134	 140	 139	 138	 125	 125	 112
-partition: 0087	total: 1324	per_label:  131	 136	 142	 125	 134	 136	 122	 135	 119	 144
-partition: 0088	total: 1377	per_label:  141	 123	 162	 134	 146	 125	 138	 125	 142	 141
-partition: 0089	total: 1432	per_label:  137	 149	 120	 135	 167	 149	 159	 137	 141	 138
-partition: 0090	total: 1490	per_label:  145	 125	 155	 163	 147	 159	 143	 149	 145	 159
-partition: 0091	total: 1549	per_label:  167	 173	 137	 171	 152	 153	 140	 150	 172	 134
-partition: 0092	total: 1611	per_label:  155	 155	 161	 179	 143	 159	 173	 168	 148	 170
-partition: 0093	total: 1676	per_label:  171	 175	 181	 157	 175	 141	 156	 162	 186	 172
-partition: 0094	total: 1743	per_label:  167	 176	 197	 166	 174	 175	 176	 175	 147	 190
-partition: 0095	total: 1813	per_label:  195	 176	 179	 182	 168	 175	 172	 190	 197	 179
-partition: 0096	total: 1885	per_label:  175	 183	 199	 189	 211	 215	 166	 173	 188	 186
-partition: 0097	total: 1960	per_label:  222	 185	 184	 195	 195	 213	 192	 182	 211	 181
-partition: 0098	total: 2039	per_label:  178	 214	 212	 198	 207	 230	 199	 222	 176	 203
-partition: 0099	total: 2121	per_label:  205	 220	 210	 208	 201	 208	 232	 197	 235	 205
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_045.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_045.txt
deleted file mode 100644
index d7e7bc9f6..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-b1_045.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-b1_045
-number_of_examples_per_label_per_shard
-partition: 0000	total: 30	per_label:    2	   4	   1	   6	   3	   3	   6	   2	   0	   3
-partition: 0001	total: 31	per_label:    7	   2	   1	   2	   5	   3	   3	   4	   1	   3
-partition: 0002	total: 32	per_label:    3	   5	   2	   3	   3	   2	   2	   2	   5	   5
-partition: 0003	total: 34	per_label:    2	   3	   0	   2	   1	   6	   8	   8	   3	   1
-partition: 0004	total: 35	per_label:    3	   5	   6	   2	   5	   4	   3	   2	   2	   3
-partition: 0005	total: 37	per_label:    3	   4	   3	   4	   7	   1	   2	   3	   5	   5
-partition: 0006	total: 39	per_label:    3	   3	   6	   2	   3	   6	   4	   4	   3	   5
-partition: 0007	total: 41	per_label:    4	   6	   1	   7	   3	   2	   3	   8	   5	   2
-partition: 0008	total: 42	per_label:    7	   4	   8	   4	   4	   3	   3	   4	   3	   2
-partition: 0009	total: 44	per_label:    3	   1	   3	   4	   7	   7	   4	   7	   7	   1
-partition: 0010	total: 46	per_label:    4	   4	   3	   6	   7	   6	   4	   4	   6	   2
-partition: 0011	total: 48	per_label:    6	   7	   2	   5	   6	   7	   6	   3	   5	   1
-partition: 0012	total: 51	per_label:    4	   3	   7	   5	   5	   5	   8	   6	   5	   3
-partition: 0013	total: 53	per_label:    3	   7	   6	   4	   5	   7	   4	   4	   9	   4
-partition: 0014	total: 55	per_label:    6	   4	   6	   4	   7	   4	   4	   5	   8	   7
-partition: 0015	total: 58	per_label:    6	   7	   6	   5	   4	   6	   5	   4	   7	   8
-partition: 0016	total: 61	per_label:    8	   7	   8	   9	   4	   8	   6	   3	   5	   3
-partition: 0017	total: 63	per_label:    5	   8	   4	   4	  10	   7	   3	  10	   3	   9
-partition: 0018	total: 66	per_label:    5	   4	   4	   7	   3	  10	   9	  10	   5	   9
-partition: 0019	total: 69	per_label:    6	   7	   7	   6	  10	   5	   9	   7	   5	   7
-partition: 0020	total: 72	per_label:    4	   4	   8	   8	   8	   6	   5	   7	  13	   9
-partition: 0021	total: 76	per_label:    7	   9	   7	   4	  11	   8	   4	   9	   8	   9
-partition: 0022	total: 79	per_label:   13	   7	   7	   4	   3	   5	  16	  14	   2	   8
-partition: 0023	total: 83	per_label:    6	   6	  10	  15	   8	   6	  15	   7	   5	   5
-partition: 0024	total: 86	per_label:   11	  13	   9	   5	   9	   7	  10	   6	   8	   8
-partition: 0025	total: 90	per_label:   12	  12	   9	   9	  13	   8	   6	   7	   7	   7
-partition: 0026	total: 94	per_label:   13	   8	   4	   7	  14	  10	   8	  10	  13	   7
-partition: 0027	total: 99	per_label:    9	  10	   5	   9	  11	  10	   9	  10	  10	  16
-partition: 0028	total: 103	per_label:   11	  11	  12	   7	  13	   6	  14	   7	   8	  14
-partition: 0029	total: 108	per_label:   15	  15	  12	   5	  12	  12	  11	   9	  11	   6
-partition: 0030	total: 113	per_label:    9	  16	   9	  18	  11	  14	  10	  10	   9	   7
-partition: 0031	total: 118	per_label:   10	  13	   9	  11	   6	  19	  10	  16	  11	  13
-partition: 0032	total: 123	per_label:   11	   8	   9	  13	  16	  12	  10	  17	  11	  16
-partition: 0033	total: 128	per_label:   18	  19	   8	  15	   7	  15	  10	  11	  20	   5
-partition: 0034	total: 134	per_label:   11	  18	   9	  15	  14	  17	  13	  16	  14	   7
-partition: 0035	total: 140	per_label:   16	  11	  21	  13	  12	  11	  17	  15	   9	  15
-partition: 0036	total: 147	per_label:   12	  15	  11	  14	  15	  14	  17	  15	  15	  19
-partition: 0037	total: 153	per_label:   16	  18	  13	  13	  14	  12	  16	  16	  20	  15
-partition: 0038	total: 160	per_label:   18	  16	  13	  11	  20	  13	  20	  15	  15	  19
-partition: 0039	total: 167	per_label:   16	  20	  16	  16	  11	  22	  20	  18	  10	  18
-partition: 0040	total: 175	per_label:   16	  23	  19	  11	  17	  19	  16	  17	  19	  18
-partition: 0041	total: 183	per_label:   22	  17	  10	  21	  22	  22	  20	  20	  13	  16
-partition: 0042	total: 191	per_label:   22	  16	  19	  20	  18	  15	  19	  18	  20	  24
-partition: 0043	total: 200	per_label:   19	  19	  21	  23	  19	  23	  20	  23	  18	  15
-partition: 0044	total: 209	per_label:   17	  21	  25	  19	  16	  25	  27	  20	  16	  23
-partition: 0045	total: 218	per_label:   30	  22	  28	  23	  24	  10	  20	  22	  20	  19
-partition: 0046	total: 228	per_label:   20	  18	  24	  24	  24	  19	  33	  24	  23	  19
-partition: 0047	total: 238	per_label:   19	  25	  19	  22	  19	  24	  25	  23	  29	  33
-partition: 0048	total: 249	per_label:   22	  23	  32	  30	  33	  18	  23	  22	  22	  24
-partition: 0049	total: 260	per_label:   31	  24	  30	  30	  23	  30	  25	  28	  18	  21
-partition: 0050	total: 272	per_label:   28	  29	  27	  31	  31	  28	  25	  24	  30	  19
-partition: 0051	total: 284	per_label:   32	  22	  32	  39	  20	  28	  31	  27	  26	  27
-partition: 0052	total: 297	per_label:   29	  37	  36	  23	  28	  30	  27	  22	  37	  28
-partition: 0053	total: 311	per_label:   32	  31	  35	  32	  28	  26	  31	  28	  38	  30
-partition: 0054	total: 325	per_label:   31	  29	  32	  31	  29	  36	  38	  29	  41	  29
-partition: 0055	total: 339	per_label:   35	  39	  38	  35	  37	  31	  21	  33	  35	  35
-partition: 0056	total: 355	per_label:   38	  45	  25	  36	  34	  33	  35	  44	  29	  36
-partition: 0057	total: 370	per_label:   32	  38	  21	  37	  35	  36	  47	  39	  44	  41
-partition: 0058	total: 387	per_label:   35	  39	  44	  41	  34	  46	  34	  46	  30	  38
-partition: 0059	total: 405	per_label:   33	  44	  43	  31	  42	  53	  28	  34	  37	  60
-partition: 0060	total: 423	per_label:   42	  35	  42	  42	  44	  43	  42	  49	  42	  42
-partition: 0061	total: 442	per_label:   36	  36	  40	  48	  47	  60	  36	  40	  55	  44
-partition: 0062	total: 462	per_label:   45	  44	  48	  38	  46	  53	  52	  52	  47	  37
-partition: 0063	total: 483	per_label:   52	  48	  42	  47	  45	  53	  49	  53	  45	  49
-partition: 0064	total: 504	per_label:   52	  53	  53	  59	  44	  56	  48	  39	  47	  53
-partition: 0065	total: 527	per_label:   51	  41	  58	  54	  67	  56	  46	  50	  52	  52
-partition: 0066	total: 551	per_label:   52	  58	  51	  55	  61	  58	  45	  67	  43	  61
-partition: 0067	total: 576	per_label:   63	  64	  52	  61	  58	  43	  70	  57	  53	  55
-partition: 0068	total: 602	per_label:   69	  61	  68	  58	  47	  50	  71	  60	  68	  50
-partition: 0069	total: 629	per_label:   71	  56	  66	  80	  57	  54	  62	  63	  68	  52
-partition: 0070	total: 657	per_label:   51	  72	  83	  52	  50	  66	  70	  73	  64	  76
-partition: 0071	total: 687	per_label:   73	  64	  78	  64	  75	  77	  64	  61	  59	  72
-partition: 0072	total: 717	per_label:   56	  75	  78	  70	  62	  74	  73	  69	  79	  81
-partition: 0073	total: 750	per_label:   77	  73	  61	  80	  70	  69	  67	  75	  89	  89
-partition: 0074	total: 784	per_label:   68	  67	  80	  88	  80	  65	  79	  86	  83	  88
-partition: 0075	total: 819	per_label:   70	  88	  83	  87	  88	  78	  86	  92	  67	  80
-partition: 0076	total: 856	per_label:   96	  88	  71	  82	  97	  72	  86	  86	  85	  93
-partition: 0077	total: 894	per_label:   97	  90	  97	  94	  86	  85	  79	  88	  87	  91
-partition: 0078	total: 934	per_label:  100	  93	  84	  81	  91	  88	 103	 107	  94	  93
-partition: 0079	total: 977	per_label:  103	  93	  95	 112	  95	  80	 102	  98	  81	 118
-partition: 0080	total: 1021	per_label:  103	  96	 106	 107	  97	 100	  94	 105	  97	 116
-partition: 0081	total: 1066	per_label:  100	 113	 101	 111	  85	  96	 110	 101	 130	 119
-partition: 0082	total: 1115	per_label:  120	 138	 120	 108	 121	 106	 105	  90	  94	 113
-partition: 0083	total: 1165	per_label:  119	 117	 104	 108	 111	 114	 126	 123	 128	 115
-partition: 0084	total: 1217	per_label:  144	 115	 104	 112	 134	 120	 121	 126	 124	 117
-partition: 0085	total: 1272	per_label:  133	 124	 120	 130	 131	 137	 135	 127	 131	 104
-partition: 0086	total: 1329	per_label:  117	 132	 152	 128	 134	 136	 128	 132	 150	 120
-partition: 0087	total: 1389	per_label:  141	 143	 136	 139	 132	 147	 158	 146	 129	 118
-partition: 0088	total: 1452	per_label:  144	 138	 159	 134	 163	 154	 140	 135	 128	 157
-partition: 0089	total: 1517	per_label:  151	 131	 171	 153	 163	 139	 150	 157	 151	 151
-partition: 0090	total: 1585	per_label:  153	 167	 137	 148	 181	 161	 171	 146	 168	 153
-partition: 0091	total: 1657	per_label:  164	 142	 170	 175	 165	 179	 160	 161	 168	 173
-partition: 0092	total: 1731	per_label:  185	 191	 163	 193	 166	 157	 157	 171	 184	 164
-partition: 0093	total: 1809	per_label:  175	 173	 195	 196	 156	 183	 196	 182	 180	 173
-partition: 0094	total: 1890	per_label:  188	 205	 187	 172	 211	 174	 184	 176	 185	 208
-partition: 0095	total: 1976	per_label:  199	 184	 215	 201	 190	 189	 192	 218	 195	 193
-partition: 0096	total: 2064	per_label:  192	 203	 222	 210	 214	 225	 175	 199	 206	 218
-partition: 0097	total: 2157	per_label:  242	 199	 203	 204	 211	 251	 215	 202	 223	 207
-partition: 0098	total: 2254	per_label:  216	 237	 222	 231	 237	 240	 214	 243	 203	 211
-partition: 0099	total: 2356	per_label:  219	 248	 238	 226	 225	 231	 257	 217	 262	 233
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-iid-balanced.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-iid-balanced.txt
deleted file mode 100644
index b5fd45e96..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-iid-balanced.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-iid-balanced
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0001	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0002	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0003	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0004	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0005	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0006	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0007	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0008	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0009	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0010	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0011	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0012	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0013	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0014	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0015	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0016	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0017	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0018	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0019	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0020	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0021	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0022	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0023	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0024	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0025	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0026	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0027	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0028	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0029	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0030	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0031	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0032	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0033	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0034	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0035	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0036	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0037	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0038	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0039	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0040	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0041	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0042	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0043	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0044	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0045	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0046	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0047	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0048	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0049	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0050	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0051	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0052	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0053	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0054	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0055	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0056	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0057	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0058	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0059	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0060	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0061	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0062	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0063	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0064	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0065	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0066	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0067	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0068	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0069	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0070	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0071	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0072	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0073	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0074	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0075	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0076	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0077	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0078	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0079	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0080	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0081	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0082	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0083	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0084	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0085	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0086	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0087	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0088	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0089	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0090	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0091	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0092	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0093	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0094	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0095	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0096	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0097	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0098	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: 0099	total: 540	per_label:   54	  54	  54	  54	  54	  54	  54	  54	  54	  54
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-01cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-01cpp.txt
deleted file mode 100644
index 62bccc43c..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-01cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-01cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0001	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0003	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0004	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0005	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0006	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0007	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0008	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0009	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0010	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0011	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0012	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0013	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0015	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0017	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0018	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0019	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0020	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0021	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0022	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0023	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0024	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0025	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0026	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0027	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0028	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0029	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0030	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0031	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0032	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0033	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0034	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0035	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0036	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0037	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0038	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0039	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0040	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0041	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0042	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0044	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0045	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0046	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0048	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0049	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0050	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0051	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0052	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0053	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0054	total: 540	per_label:    0	   0	 540	   0	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0056	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0057	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0058	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0059	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0060	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0061	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0062	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0063	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0064	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0065	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0066	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0067	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0068	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0069	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0070	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0071	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0072	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0073	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0074	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0075	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0076	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0077	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0078	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0079	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	   0	 540
-partition: 0080	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0081	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0082	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0083	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0084	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0085	total: 540	per_label:  540	   0	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0086	total: 540	per_label:    0	 540	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0087	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0088	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: 0089	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0090	total: 540	per_label:    0	   0	   0	   0	   0	 540	   0	   0	   0	   0
-partition: 0091	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0092	total: 540	per_label:    0	   0	   0	   0	 540	   0	   0	   0	   0	   0
-partition: 0093	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0094	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0095	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0096	total: 540	per_label:    0	   0	   0	   0	   0	   0	 540	   0	   0	   0
-partition: 0097	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 540	   0	   0
-partition: 0098	total: 540	per_label:    0	   0	   0	 540	   0	   0	   0	   0	   0	   0
-partition: 0099	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 540	   0
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-02cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-02cpp.txt
deleted file mode 100644
index 168ed2e43..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-02cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-02cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	   0	 270
-partition: 0001	total: 540	per_label:    0	 270	   0	   0	   0	   0	 270	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	   0	 270	   0	   0	   0	 270	   0	   0	   0
-partition: 0003	total: 540	per_label:  270	   0	   0	   0	   0	   0	 270	   0	   0	   0
-partition: 0004	total: 540	per_label:    0	   0	   0	 270	   0	 270	   0	   0	   0	   0
-partition: 0005	total: 540	per_label:    0	 270	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0006	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	   0	 270
-partition: 0007	total: 540	per_label:    0	   0	 270	   0	   0	 270	   0	   0	   0	   0
-partition: 0008	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0009	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	   0	 270	   0
-partition: 0010	total: 540	per_label:    0	   0	 270	   0	   0	 270	   0	   0	   0	   0
-partition: 0011	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 270	   0	 270
-partition: 0012	total: 540	per_label:    0	   0	   0	   0	 270	   0	 270	   0	   0	   0
-partition: 0013	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	 270	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	 270	   0	 270	   0	   0	   0	   0	   0
-partition: 0015	total: 540	per_label:  270	   0	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	   0	 270	   0
-partition: 0017	total: 540	per_label:  270	 270	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0018	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0019	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	 270	   0
-partition: 0020	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	   0	 270	   0
-partition: 0021	total: 540	per_label:    0	 270	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0022	total: 540	per_label:    0	   0	   0	   0	   0	 270	 270	   0	   0	   0
-partition: 0023	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	 270	   0	   0
-partition: 0024	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	   0	 270	   0
-partition: 0025	total: 540	per_label:    0	   0	 270	   0	   0	 270	   0	   0	   0	   0
-partition: 0026	total: 540	per_label:    0	 270	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0027	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	 270	   0	   0
-partition: 0028	total: 540	per_label:    0	   0	 270	   0	   0	 270	   0	   0	   0	   0
-partition: 0029	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	 270	   0	   0
-partition: 0030	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	   0	 270	   0
-partition: 0031	total: 540	per_label:  270	   0	 270	   0	   0	   0	   0	   0	   0	   0
-partition: 0032	total: 540	per_label:    0	   0	 270	   0	   0	 270	   0	   0	   0	   0
-partition: 0033	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	   0	 270	   0
-partition: 0034	total: 540	per_label:    0	   0	   0	 270	   0	   0	 270	   0	   0	   0
-partition: 0035	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	 270	   0	   0
-partition: 0036	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	 270	   0	   0
-partition: 0037	total: 540	per_label:    0	   0	 270	 270	   0	   0	   0	   0	   0	   0
-partition: 0038	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	 270	   0	   0
-partition: 0039	total: 540	per_label:  270	   0	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0040	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	   0	   0	 270
-partition: 0041	total: 540	per_label:    0	   0	 270	   0	   0	   0	   0	   0	   0	 270
-partition: 0042	total: 540	per_label:    0	 270	 270	   0	   0	   0	   0	   0	   0	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	   0	   0	 270
-partition: 0044	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	   0	   0	 270
-partition: 0045	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	   0	 270
-partition: 0046	total: 540	per_label:  270	   0	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0048	total: 540	per_label:  270	   0	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0049	total: 540	per_label:  270	   0	   0	   0	   0	   0	 270	   0	   0	   0
-partition: 0050	total: 540	per_label:    0	   0	   0	 270	   0	 270	   0	   0	   0	   0
-partition: 0051	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	 270	   0
-partition: 0052	total: 540	per_label:    0	 270	   0	   0	   0	 270	   0	   0	   0	   0
-partition: 0053	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	   0	 270	   0
-partition: 0054	total: 540	per_label:    0	 270	 270	   0	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 540	per_label:    0	 270	   0	   0	   0	 270	   0	   0	   0	   0
-partition: 0056	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 270	 270
-partition: 0057	total: 540	per_label:    0	 270	 270	   0	   0	   0	   0	   0	   0	   0
-partition: 0058	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	   0	 270	   0
-partition: 0059	total: 540	per_label:  270	   0	   0	   0	   0	   0	 270	   0	   0	   0
-partition: 0060	total: 540	per_label:    0	 270	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0061	total: 540	per_label:  270	   0	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0062	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	   0	 270
-partition: 0063	total: 540	per_label:    0	 270	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0064	total: 540	per_label:  270	   0	   0	   0	 270	   0	   0	   0	   0	   0
-partition: 0065	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	 270	   0	   0
-partition: 0066	total: 540	per_label:  270	   0	 270	   0	   0	   0	   0	   0	   0	   0
-partition: 0067	total: 540	per_label:    0	   0	 270	 270	   0	   0	   0	   0	   0	   0
-partition: 0068	total: 540	per_label:    0	 270	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0069	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	   0	 270	 270
-partition: 0070	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	   0	 270
-partition: 0071	total: 540	per_label:    0	 270	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0072	total: 540	per_label:    0	   0	   0	   0	 270	 270	   0	   0	   0	   0
-partition: 0073	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	   0	 270
-partition: 0074	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0075	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	   0	   0	 270
-partition: 0076	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	 270	   0
-partition: 0077	total: 540	per_label:  270	   0	   0	   0	   0	   0	   0	   0	   0	 270
-partition: 0078	total: 540	per_label:  270	   0	   0	 270	   0	   0	   0	   0	   0	   0
-partition: 0079	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	 270	   0
-partition: 0080	total: 540	per_label:  270	 270	   0	   0	   0	   0	   0	   0	   0	   0
-partition: 0081	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	 270	   0	   0
-partition: 0082	total: 540	per_label:    0	   0	   0	   0	   0	 270	 270	   0	   0	   0
-partition: 0083	total: 540	per_label:    0	   0	   0	   0	 270	   0	   0	   0	 270	   0
-partition: 0084	total: 540	per_label:    0	   0	   0	 270	 270	   0	   0	   0	   0	   0
-partition: 0085	total: 540	per_label:    0	   0	   0	 270	   0	 270	   0	   0	   0	   0
-partition: 0086	total: 540	per_label:    0	   0	   0	 270	 270	   0	   0	   0	   0	   0
-partition: 0087	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	   0	 270	   0
-partition: 0088	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	 270	   0	   0
-partition: 0089	total: 540	per_label:    0	   0	   0	   0	   0	 270	   0	   0	 270	   0
-partition: 0090	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	   0	 270	   0
-partition: 0091	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	 270	   0	   0
-partition: 0092	total: 540	per_label:    0	   0	   0	 270	   0	   0	   0	 270	   0	   0
-partition: 0093	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 270	 270	   0
-partition: 0094	total: 540	per_label:    0	   0	   0	   0	   0	   0	   0	 270	 270	   0
-partition: 0095	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	 270	   0	   0
-partition: 0096	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	 270	   0	   0
-partition: 0097	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	 270	   0	   0
-partition: 0098	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	 270	   0	   0
-partition: 0099	total: 540	per_label:    0	   0	   0	   0	   0	   0	 270	 270	   0	   0
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-03cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-03cpp.txt
deleted file mode 100644
index eb1a14433..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-03cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-03cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:    0	   0	   0	 180	 180	   0	   0	   0	   0	 180
-partition: 0001	total: 540	per_label:    0	 180	   0	   0	   0	 180	 180	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	   0	 180	   0	   0	 180	 180	   0	   0	   0
-partition: 0003	total: 540	per_label:  180	   0	   0	   0	 180	   0	 180	   0	   0	   0
-partition: 0004	total: 540	per_label:    0	   0	   0	 180	   0	 180	   0	 180	   0	   0
-partition: 0005	total: 540	per_label:    0	 180	 180	   0	 180	   0	   0	   0	   0	   0
-partition: 0006	total: 540	per_label:    0	   0	   0	   0	 180	 180	   0	   0	   0	 180
-partition: 0007	total: 540	per_label:    0	   0	 180	   0	   0	 180	   0	   0	   0	 180
-partition: 0008	total: 540	per_label:    0	 180	   0	   0	   0	   0	   0	   0	 180	 180
-partition: 0009	total: 540	per_label:    0	   0	 180	   0	   0	   0	   0	   0	 180	 180
-partition: 0010	total: 540	per_label:  180	   0	 180	   0	   0	 180	   0	   0	   0	   0
-partition: 0011	total: 540	per_label:    0	   0	   0	   0	   0	 180	   0	 180	   0	 180
-partition: 0012	total: 540	per_label:    0	   0	   0	   0	 180	 180	 180	   0	   0	   0
-partition: 0013	total: 540	per_label:    0	 180	   0	 180	   0	   0	   0	 180	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	 180	   0	 180	   0	   0	 180	   0	   0
-partition: 0015	total: 540	per_label:  180	 180	   0	 180	   0	   0	   0	   0	   0	   0
-partition: 0016	total: 540	per_label:    0	   0	 180	   0	   0	 180	   0	   0	 180	   0
-partition: 0017	total: 540	per_label:  180	 180	   0	   0	   0	   0	   0	   0	   0	 180
-partition: 0018	total: 540	per_label:    0	 180	 180	   0	   0	   0	   0	   0	   0	 180
-partition: 0019	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	   0	 180	   0
-partition: 0020	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	   0	 180	   0
-partition: 0021	total: 540	per_label:    0	 180	   0	 180	   0	 180	   0	   0	   0	   0
-partition: 0022	total: 540	per_label:    0	 180	   0	   0	   0	 180	 180	   0	   0	   0
-partition: 0023	total: 540	per_label:  180	   0	 180	   0	   0	   0	   0	 180	   0	   0
-partition: 0024	total: 540	per_label:    0	 180	   0	   0	   0	   0	 180	   0	 180	   0
-partition: 0025	total: 540	per_label:    0	   0	 180	   0	   0	 180	   0	 180	   0	   0
-partition: 0026	total: 540	per_label:    0	 180	   0	   0	 180	 180	   0	   0	   0	   0
-partition: 0027	total: 540	per_label:    0	 180	   0	 180	   0	   0	   0	 180	   0	   0
-partition: 0028	total: 540	per_label:    0	   0	 180	   0	 180	 180	   0	   0	   0	   0
-partition: 0029	total: 540	per_label:    0	   0	 180	   0	   0	 180	   0	 180	   0	   0
-partition: 0030	total: 540	per_label:    0	 180	   0	   0	   0	   0	 180	   0	 180	   0
-partition: 0031	total: 540	per_label:  180	   0	 180	   0	   0	   0	   0	   0	 180	   0
-partition: 0032	total: 540	per_label:  180	   0	 180	   0	   0	 180	   0	   0	   0	   0
-partition: 0033	total: 540	per_label:    0	   0	 180	 180	   0	   0	   0	   0	 180	   0
-partition: 0034	total: 540	per_label:    0	   0	   0	 180	   0	   0	 180	   0	 180	   0
-partition: 0035	total: 540	per_label:    0	   0	 180	 180	   0	   0	   0	 180	   0	   0
-partition: 0036	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0037	total: 540	per_label:    0	   0	 180	 180	   0	   0	   0	   0	 180	   0
-partition: 0038	total: 540	per_label:    0	   0	   0	   0	 180	   0	 180	 180	   0	   0
-partition: 0039	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0040	total: 540	per_label:    0	   0	   0	 180	   0	   0	 180	   0	   0	 180
-partition: 0041	total: 540	per_label:  180	   0	 180	   0	   0	   0	   0	   0	   0	 180
-partition: 0042	total: 540	per_label:    0	 180	 180	   0	   0	   0	   0	 180	   0	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	   0	   0	 180	   0	   0	 180	 180
-partition: 0044	total: 540	per_label:    0	 180	   0	   0	   0	 180	   0	   0	   0	 180
-partition: 0045	total: 540	per_label:    0	 180	   0	   0	   0	   0	 180	   0	   0	 180
-partition: 0046	total: 540	per_label:  180	   0	 180	 180	   0	   0	   0	   0	   0	   0
-partition: 0047	total: 540	per_label:  180	   0	   0	 180	   0	   0	   0	   0	   0	 180
-partition: 0048	total: 540	per_label:  180	   0	   0	 180	   0	   0	   0	   0	   0	 180
-partition: 0049	total: 540	per_label:  180	 180	   0	   0	   0	   0	 180	   0	   0	   0
-partition: 0050	total: 540	per_label:    0	 180	   0	 180	   0	 180	   0	   0	   0	   0
-partition: 0051	total: 540	per_label:    0	   0	   0	   0	   0	   0	 180	   0	 180	 180
-partition: 0052	total: 540	per_label:  180	 180	   0	   0	   0	 180	   0	   0	   0	   0
-partition: 0053	total: 540	per_label:  180	   0	   0	   0	   0	   0	   0	 180	 180	   0
-partition: 0054	total: 540	per_label:    0	 180	 180	 180	   0	   0	   0	   0	   0	   0
-partition: 0055	total: 540	per_label:  180	 180	   0	   0	   0	 180	   0	   0	   0	   0
-partition: 0056	total: 540	per_label:  180	   0	   0	   0	   0	   0	   0	   0	 180	 180
-partition: 0057	total: 540	per_label:    0	 180	 180	   0	   0	   0	   0	   0	 180	   0
-partition: 0058	total: 540	per_label:    0	   0	   0	   0	   0	 180	 180	   0	 180	   0
-partition: 0059	total: 540	per_label:  180	   0	 180	   0	   0	   0	 180	   0	   0	   0
-partition: 0060	total: 540	per_label:    0	 180	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0061	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0062	total: 540	per_label:    0	 180	   0	   0	 180	   0	   0	   0	   0	 180
-partition: 0063	total: 540	per_label:    0	 180	   0	 180	 180	   0	   0	   0	   0	   0
-partition: 0064	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0065	total: 540	per_label:    0	 180	   0	   0	   0	 180	   0	 180	   0	   0
-partition: 0066	total: 540	per_label:  180	   0	 180	   0	 180	   0	   0	   0	   0	   0
-partition: 0067	total: 540	per_label:    0	   0	 180	 180	 180	   0	   0	   0	   0	   0
-partition: 0068	total: 540	per_label:    0	 180	 180	   0	   0	   0	 180	   0	   0	   0
-partition: 0069	total: 540	per_label:    0	   0	   0	   0	   0	   0	 180	 180	 180	   0
-partition: 0070	total: 540	per_label:    0	   0	   0	   0	   0	   0	 180	   0	 180	 180
-partition: 0071	total: 540	per_label:  180	   0	 180	 180	   0	   0	   0	   0	   0	   0
-partition: 0072	total: 540	per_label:    0	   0	   0	 180	   0	   0	   0	 180	   0	 180
-partition: 0073	total: 540	per_label:    0	 180	   0	   0	 180	   0	 180	   0	   0	   0
-partition: 0074	total: 540	per_label:    0	   0	   0	   0	 180	 180	   0	 180	   0	   0
-partition: 0075	total: 540	per_label:    0	   0	   0	 180	   0	   0	   0	 180	 180	   0
-partition: 0076	total: 540	per_label:  180	 180	 180	   0	   0	   0	   0	   0	   0	   0
-partition: 0077	total: 540	per_label:    0	   0	   0	   0	   0	 180	   0	   0	 180	 180
-partition: 0078	total: 540	per_label:  180	   0	   0	 180	   0	   0	   0	   0	   0	 180
-partition: 0079	total: 540	per_label:    0	 180	   0	   0	   0	   0	 180	   0	   0	 180
-partition: 0080	total: 540	per_label:  180	   0	   0	 180	   0	   0	   0	 180	   0	   0
-partition: 0081	total: 540	per_label:    0	   0	   0	   0	   0	 180	   0	   0	 180	 180
-partition: 0082	total: 540	per_label:    0	   0	 180	   0	   0	 180	   0	 180	   0	   0
-partition: 0083	total: 540	per_label:    0	   0	   0	 180	   0	   0	   0	 180	   0	 180
-partition: 0084	total: 540	per_label:  180	   0	   0	   0	 180	   0	   0	 180	   0	   0
-partition: 0085	total: 540	per_label:  180	   0	 180	   0	   0	   0	   0	   0	 180	   0
-partition: 0086	total: 540	per_label:    0	   0	   0	   0	   0	 180	 180	   0	 180	   0
-partition: 0087	total: 540	per_label:    0	   0	   0	 180	   0	   0	 180	   0	 180	   0
-partition: 0088	total: 540	per_label:    0	   0	   0	   0	   0	 180	 180	   0	   0	 180
-partition: 0089	total: 540	per_label:    0	   0	   0	 180	 180	   0	   0	   0	 180	   0
-partition: 0090	total: 540	per_label:    0	   0	   0	   0	   0	 180	   0	 180	 180	   0
-partition: 0091	total: 540	per_label:    0	   0	   0	   0	   0	   0	 180	 180	   0	 180
-partition: 0092	total: 540	per_label:    0	   0	   0	 180	 180	   0	   0	   0	   0	 180
-partition: 0093	total: 540	per_label:    0	   0	   0	   0	 180	   0	   0	   0	 180	 180
-partition: 0094	total: 540	per_label:    0	   0	   0	   0	 180	   0	 180	 180	   0	   0
-partition: 0095	total: 540	per_label:    0	   0	   0	 180	 180	   0	 180	   0	   0	   0
-partition: 0096	total: 540	per_label:    0	   0	   0	   0	 180	   0	 180	   0	   0	 180
-partition: 0097	total: 540	per_label:    0	   0	   0	 180	 180	   0	 180	   0	   0	   0
-partition: 0098	total: 540	per_label:    0	   0	   0	 180	   0	   0	 180	   0	 180	   0
-partition: 0099	total: 540	per_label:    0	   0	   0	   0	   0	   0	 180	 180	 180	   0
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-04cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-04cpp.txt
deleted file mode 100644
index 8ccea7599..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-04cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-04cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:    0	 135	   0	 135	 135	   0	   0	   0	   0	 135
-partition: 0001	total: 540	per_label:    0	 135	 135	   0	   0	 135	 135	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	   0	 135	   0	   0	 135	 135	 135	   0	   0
-partition: 0003	total: 540	per_label:  135	   0	   0	   0	 135	   0	 135	   0	   0	 135
-partition: 0004	total: 540	per_label:    0	   0	   0	 135	   0	 135	   0	 135	   0	 135
-partition: 0005	total: 540	per_label:    0	 135	 135	   0	 135	   0	   0	 135	   0	   0
-partition: 0006	total: 540	per_label:    0	   0	   0	   0	 135	 135	   0	 135	   0	 135
-partition: 0007	total: 540	per_label:    0	   0	 135	   0	 135	 135	   0	   0	   0	 135
-partition: 0008	total: 540	per_label:    0	 135	   0	   0	   0	   0	 135	   0	 135	 135
-partition: 0009	total: 540	per_label:    0	   0	 135	   0	   0	 135	   0	   0	 135	 135
-partition: 0010	total: 540	per_label:  135	   0	 135	 135	   0	 135	   0	   0	   0	   0
-partition: 0011	total: 540	per_label:    0	   0	   0	   0	 135	 135	   0	 135	   0	 135
-partition: 0012	total: 540	per_label:    0	   0	   0	   0	 135	 135	 135	   0	   0	 135
-partition: 0013	total: 540	per_label:  135	 135	   0	 135	   0	   0	   0	 135	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	 135	 135	 135	   0	   0	 135	   0	   0
-partition: 0015	total: 540	per_label:  135	 135	   0	 135	   0	   0	 135	   0	   0	   0
-partition: 0016	total: 540	per_label:    0	 135	 135	   0	   0	 135	   0	   0	 135	   0
-partition: 0017	total: 540	per_label:  135	 135	   0	   0	   0	   0	 135	   0	   0	 135
-partition: 0018	total: 540	per_label:    0	 135	 135	   0	   0	 135	   0	   0	   0	 135
-partition: 0019	total: 540	per_label:  135	   0	 135	   0	 135	   0	   0	   0	 135	   0
-partition: 0020	total: 540	per_label:  135	   0	   0	   0	 135	 135	   0	   0	 135	   0
-partition: 0021	total: 540	per_label:    0	 135	   0	 135	   0	 135	   0	   0	 135	   0
-partition: 0022	total: 540	per_label:    0	 135	   0	   0	   0	 135	 135	   0	 135	   0
-partition: 0023	total: 540	per_label:  135	   0	 135	 135	   0	   0	   0	 135	   0	   0
-partition: 0024	total: 540	per_label:    0	 135	   0	   0	   0	   0	 135	   0	 135	 135
-partition: 0025	total: 540	per_label:    0	 135	 135	   0	   0	 135	   0	 135	   0	   0
-partition: 0026	total: 540	per_label:    0	 135	   0	 135	 135	 135	   0	   0	   0	   0
-partition: 0027	total: 540	per_label:    0	 135	   0	 135	   0	   0	 135	 135	   0	   0
-partition: 0028	total: 540	per_label:    0	   0	 135	 135	 135	 135	   0	   0	   0	   0
-partition: 0029	total: 540	per_label:    0	   0	 135	   0	   0	 135	   0	 135	   0	 135
-partition: 0030	total: 540	per_label:    0	 135	 135	   0	   0	   0	   0	 135	 135	   0
-partition: 0031	total: 540	per_label:  135	   0	 135	   0	 135	   0	   0	   0	 135	   0
-partition: 0032	total: 540	per_label:  135	   0	 135	   0	   0	 135	   0	   0	 135	   0
-partition: 0033	total: 540	per_label:    0	   0	 135	 135	   0	   0	   0	 135	 135	   0
-partition: 0034	total: 540	per_label:    0	   0	   0	 135	 135	   0	 135	   0	 135	   0
-partition: 0035	total: 540	per_label:    0	   0	 135	 135	   0	 135	   0	 135	   0	   0
-partition: 0036	total: 540	per_label:  135	   0	   0	   0	 135	   0	 135	 135	   0	   0
-partition: 0037	total: 540	per_label:    0	 135	 135	 135	   0	   0	   0	   0	 135	   0
-partition: 0038	total: 540	per_label:    0	   0	   0	   0	 135	   0	 135	 135	 135	   0
-partition: 0039	total: 540	per_label:  135	   0	   0	   0	 135	 135	   0	 135	   0	   0
-partition: 0040	total: 540	per_label:  135	   0	   0	 135	   0	   0	 135	   0	   0	 135
-partition: 0041	total: 540	per_label:  135	 135	 135	   0	   0	   0	   0	   0	   0	 135
-partition: 0042	total: 540	per_label:    0	 135	 135	   0	   0	   0	   0	 135	 135	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	 135	   0	 135	   0	   0	 135	 135
-partition: 0044	total: 540	per_label:    0	 135	   0	   0	   0	 135	   0	   0	 135	 135
-partition: 0045	total: 540	per_label:    0	 135	   0	   0	   0	   0	 135	   0	 135	 135
-partition: 0046	total: 540	per_label:  135	   0	 135	 135	   0	 135	   0	   0	   0	   0
-partition: 0047	total: 540	per_label:  135	   0	   0	 135	 135	   0	   0	   0	   0	 135
-partition: 0048	total: 540	per_label:  135	   0	   0	 135	   0	   0	   0	 135	   0	 135
-partition: 0049	total: 540	per_label:  135	 135	   0	   0	   0	   0	 135	   0	   0	 135
-partition: 0050	total: 540	per_label:    0	 135	   0	 135	   0	 135	   0	   0	   0	 135
-partition: 0051	total: 540	per_label:    0	   0	   0	   0	 135	   0	 135	   0	 135	 135
-partition: 0052	total: 540	per_label:  135	 135	   0	   0	 135	 135	   0	   0	   0	   0
-partition: 0053	total: 540	per_label:  135	   0	   0	   0	   0	 135	   0	 135	 135	   0
-partition: 0054	total: 540	per_label:    0	 135	 135	 135	   0	 135	   0	   0	   0	   0
-partition: 0055	total: 540	per_label:  135	 135	   0	   0	   0	 135	 135	   0	   0	   0
-partition: 0056	total: 540	per_label:  135	   0	   0	   0	   0	   0	 135	   0	 135	 135
-partition: 0057	total: 540	per_label:    0	 135	 135	   0	   0	   0	   0	   0	 135	 135
-partition: 0058	total: 540	per_label:    0	   0	   0	   0	 135	 135	 135	   0	 135	   0
-partition: 0059	total: 540	per_label:  135	 135	 135	   0	   0	   0	 135	   0	   0	   0
-partition: 0060	total: 540	per_label:    0	 135	 135	   0	 135	   0	   0	 135	   0	   0
-partition: 0061	total: 540	per_label:  135	 135	   0	   0	 135	   0	   0	 135	   0	   0
-partition: 0062	total: 540	per_label:    0	 135	 135	   0	 135	   0	   0	   0	   0	 135
-partition: 0063	total: 540	per_label:    0	 135	   0	 135	 135	   0	   0	 135	   0	   0
-partition: 0064	total: 540	per_label:  135	   0	   0	   0	 135	   0	 135	 135	   0	   0
-partition: 0065	total: 540	per_label:    0	 135	   0	   0	 135	 135	   0	 135	   0	   0
-partition: 0066	total: 540	per_label:  135	   0	 135	 135	 135	   0	   0	   0	   0	   0
-partition: 0067	total: 540	per_label:    0	 135	 135	 135	 135	   0	   0	   0	   0	   0
-partition: 0068	total: 540	per_label:    0	 135	 135	   0	   0	   0	 135	   0	 135	   0
-partition: 0069	total: 540	per_label:    0	 135	   0	   0	   0	   0	 135	 135	 135	   0
-partition: 0070	total: 540	per_label:    0	   0	   0	   0	   0	 135	 135	   0	 135	 135
-partition: 0071	total: 540	per_label:  135	   0	 135	 135	 135	   0	   0	   0	   0	   0
-partition: 0072	total: 540	per_label:    0	   0	   0	 135	   0	 135	   0	 135	   0	 135
-partition: 0073	total: 540	per_label:    0	 135	   0	   0	 135	   0	 135	   0	 135	   0
-partition: 0074	total: 540	per_label:    0	   0	   0	   0	 135	 135	 135	 135	   0	   0
-partition: 0075	total: 540	per_label:    0	   0	   0	 135	   0	 135	   0	 135	 135	   0
-partition: 0076	total: 540	per_label:  135	 135	 135	 135	   0	   0	   0	   0	   0	   0
-partition: 0077	total: 540	per_label:    0	   0	   0	   0	   0	 135	 135	   0	 135	 135
-partition: 0078	total: 540	per_label:  135	   0	   0	 135	   0	   0	   0	 135	   0	 135
-partition: 0079	total: 540	per_label:    0	 135	 135	   0	   0	   0	 135	   0	   0	 135
-partition: 0080	total: 540	per_label:    0	   0	   0	   0	 135	   0	 135	 135	   0	 135
-partition: 0081	total: 540	per_label:    0	 135	   0	 135	 135	   0	   0	   0	 135	   0
-partition: 0082	total: 540	per_label:  135	   0	 135	 135	   0	   0	   0	 135	   0	   0
-partition: 0083	total: 540	per_label:  135	   0	 135	   0	 135	   0	   0	   0	 135	   0
-partition: 0084	total: 540	per_label:  135	   0	 135	   0	 135	   0	   0	   0	 135	   0
-partition: 0085	total: 540	per_label:    0	   0	   0	 135	 135	   0	   0	   0	 135	 135
-partition: 0086	total: 540	per_label:    0	   0	   0	 135	 135	 135	   0	   0	   0	 135
-partition: 0087	total: 540	per_label:  135	   0	   0	   0	   0	 135	 135	 135	   0	   0
-partition: 0088	total: 540	per_label:    0	   0	   0	   0	 135	 135	   0	   0	 135	 135
-partition: 0089	total: 540	per_label:    0	   0	   0	   0	 135	 135	 135	   0	   0	 135
-partition: 0090	total: 540	per_label:    0	   0	 135	   0	   0	   0	 135	 135	 135	   0
-partition: 0091	total: 540	per_label:    0	   0	 135	 135	   0	   0	 135	 135	   0	   0
-partition: 0092	total: 540	per_label:  135	   0	 135	   0	   0	   0	   0	 135	 135	   0
-partition: 0093	total: 540	per_label:  135	   0	   0	   0	   0	   0	 135	 135	   0	 135
-partition: 0094	total: 540	per_label:  135	   0	   0	 135	   0	   0	 135	   0	   0	 135
-partition: 0095	total: 540	per_label:    0	   0	   0	 135	   0	   0	 135	 135	   0	 135
-partition: 0096	total: 540	per_label:  135	   0	   0	 135	   0	   0	 135	 135	   0	   0
-partition: 0097	total: 540	per_label:  135	   0	   0	 135	   0	   0	 135	   0	 135	   0
-partition: 0098	total: 540	per_label:  135	   0	   0	 135	   0	   0	   0	 135	 135	   0
-partition: 0099	total: 540	per_label:  135	   0	   0	   0	   0	   0	 135	   0	 135	 135
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-05cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-05cpp.txt
deleted file mode 100644
index 2a162fba4..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-05cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-05cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:  108	 108	   0	 108	   0	   0	   0	 108	   0	 108
-partition: 0001	total: 540	per_label:    0	 108	 108	 108	   0	 108	 108	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	 108	 108	   0	   0	 108	 108	 108	   0	   0
-partition: 0003	total: 540	per_label:  108	   0	   0	   0	 108	 108	 108	   0	   0	 108
-partition: 0004	total: 540	per_label:    0	   0	 108	 108	   0	 108	   0	 108	   0	 108
-partition: 0005	total: 540	per_label:    0	 108	 108	   0	 108	   0	 108	 108	   0	   0
-partition: 0006	total: 540	per_label:    0	   0	   0	   0	 108	 108	   0	 108	 108	 108
-partition: 0007	total: 540	per_label:    0	   0	 108	   0	 108	 108	   0	 108	   0	 108
-partition: 0008	total: 540	per_label:    0	 108	   0	   0	 108	   0	 108	   0	 108	 108
-partition: 0009	total: 540	per_label:    0	 108	 108	   0	   0	 108	   0	   0	 108	 108
-partition: 0010	total: 540	per_label:  108	   0	 108	 108	   0	 108	   0	   0	 108	   0
-partition: 0011	total: 540	per_label:  108	   0	   0	   0	 108	 108	   0	 108	   0	 108
-partition: 0012	total: 540	per_label:    0	   0	   0	   0	 108	 108	 108	   0	 108	 108
-partition: 0013	total: 540	per_label:  108	 108	   0	 108	   0	   0	 108	 108	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	 108	 108	 108	   0	   0	 108	 108	   0
-partition: 0015	total: 540	per_label:  108	 108	   0	 108	   0	   0	 108	   0	 108	   0
-partition: 0016	total: 540	per_label:    0	 108	 108	   0	   0	 108	 108	   0	 108	   0
-partition: 0017	total: 540	per_label:  108	 108	   0	   0	   0	   0	 108	   0	 108	 108
-partition: 0018	total: 540	per_label:    0	 108	 108	   0	   0	 108	   0	   0	 108	 108
-partition: 0019	total: 540	per_label:  108	   0	 108	   0	 108	 108	   0	   0	 108	   0
-partition: 0020	total: 540	per_label:  108	   0	   0	   0	 108	 108	   0	 108	 108	   0
-partition: 0021	total: 540	per_label:    0	 108	   0	 108	 108	 108	   0	   0	 108	   0
-partition: 0022	total: 540	per_label:    0	 108	   0	 108	   0	 108	 108	   0	 108	   0
-partition: 0023	total: 540	per_label:  108	 108	 108	 108	   0	   0	   0	 108	   0	   0
-partition: 0024	total: 540	per_label:    0	 108	 108	   0	   0	   0	 108	   0	 108	 108
-partition: 0025	total: 540	per_label:    0	 108	 108	   0	   0	 108	   0	 108	 108	   0
-partition: 0026	total: 540	per_label:  108	 108	   0	 108	 108	 108	   0	   0	   0	   0
-partition: 0027	total: 540	per_label:    0	 108	   0	 108	   0	   0	 108	 108	   0	 108
-partition: 0028	total: 540	per_label:    0	   0	 108	 108	 108	 108	   0	   0	 108	   0
-partition: 0029	total: 540	per_label:    0	   0	 108	   0	   0	 108	   0	 108	 108	 108
-partition: 0030	total: 540	per_label:    0	 108	 108	   0	 108	   0	   0	 108	 108	   0
-partition: 0031	total: 540	per_label:  108	   0	 108	   0	 108	   0	   0	 108	 108	   0
-partition: 0032	total: 540	per_label:  108	   0	 108	   0	   0	 108	 108	   0	 108	   0
-partition: 0033	total: 540	per_label:    0	   0	 108	 108	 108	   0	   0	 108	 108	   0
-partition: 0034	total: 540	per_label:    0	   0	   0	 108	 108	 108	 108	   0	 108	   0
-partition: 0035	total: 540	per_label:    0	   0	 108	 108	   0	 108	   0	 108	 108	   0
-partition: 0036	total: 540	per_label:  108	   0	 108	   0	 108	   0	   0	 108	   0	 108
-partition: 0037	total: 540	per_label:    0	 108	 108	 108	 108	   0	   0	   0	 108	   0
-partition: 0038	total: 540	per_label:    0	   0	 108	   0	 108	   0	 108	 108	 108	   0
-partition: 0039	total: 540	per_label:  108	   0	   0	 108	 108	 108	   0	 108	   0	   0
-partition: 0040	total: 540	per_label:  108	   0	   0	 108	   0	 108	 108	   0	   0	 108
-partition: 0041	total: 540	per_label:  108	 108	 108	   0	   0	   0	   0	   0	 108	 108
-partition: 0042	total: 540	per_label:  108	 108	 108	   0	   0	   0	   0	 108	 108	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	 108	 108	 108	   0	   0	 108	 108
-partition: 0044	total: 540	per_label:    0	 108	   0	   0	   0	 108	 108	   0	 108	 108
-partition: 0045	total: 540	per_label:    0	 108	   0	   0	   0	   0	 108	 108	 108	 108
-partition: 0046	total: 540	per_label:  108	   0	 108	 108	   0	 108	   0	 108	   0	   0
-partition: 0047	total: 540	per_label:  108	   0	   0	 108	 108	   0	   0	 108	   0	 108
-partition: 0048	total: 540	per_label:  108	   0	   0	 108	   0	   0	 108	 108	   0	 108
-partition: 0049	total: 540	per_label:  108	 108	   0	   0	   0	   0	 108	 108	   0	 108
-partition: 0050	total: 540	per_label:    0	 108	 108	 108	   0	 108	   0	   0	   0	 108
-partition: 0051	total: 540	per_label:    0	   0	 108	   0	 108	   0	 108	   0	 108	 108
-partition: 0052	total: 540	per_label:  108	 108	   0	   0	 108	 108	   0	   0	 108	   0
-partition: 0053	total: 540	per_label:  108	   0	   0	   0	 108	 108	   0	 108	 108	   0
-partition: 0054	total: 540	per_label:    0	 108	 108	 108	   0	 108	 108	   0	   0	   0
-partition: 0055	total: 540	per_label:  108	 108	   0	   0	   0	 108	 108	   0	   0	 108
-partition: 0056	total: 540	per_label:  108	   0	   0	 108	   0	   0	 108	   0	 108	 108
-partition: 0057	total: 540	per_label:  108	 108	 108	   0	   0	   0	   0	   0	 108	 108
-partition: 0058	total: 540	per_label:    0	   0	   0	   0	 108	 108	 108	 108	 108	   0
-partition: 0059	total: 540	per_label:  108	 108	 108	   0	   0	   0	 108	 108	   0	   0
-partition: 0060	total: 540	per_label:    0	 108	 108	   0	 108	   0	   0	 108	 108	   0
-partition: 0061	total: 540	per_label:  108	 108	   0	   0	 108	   0	 108	 108	   0	   0
-partition: 0062	total: 540	per_label:    0	 108	 108	   0	 108	   0	   0	 108	   0	 108
-partition: 0063	total: 540	per_label:    0	 108	   0	 108	 108	   0	   0	 108	 108	   0
-partition: 0064	total: 540	per_label:  108	   0	   0	   0	 108	   0	 108	 108	   0	 108
-partition: 0065	total: 540	per_label:    0	 108	   0	   0	 108	 108	   0	 108	   0	 108
-partition: 0066	total: 540	per_label:  108	 108	 108	 108	 108	   0	   0	   0	   0	   0
-partition: 0067	total: 540	per_label:    0	 108	 108	 108	 108	   0	 108	   0	   0	   0
-partition: 0068	total: 540	per_label:    0	 108	 108	   0	   0	   0	 108	   0	 108	 108
-partition: 0069	total: 540	per_label:  108	   0	   0	 108	   0	   0	 108	 108	 108	   0
-partition: 0070	total: 540	per_label:    0	   0	   0	 108	   0	 108	 108	   0	 108	 108
-partition: 0071	total: 540	per_label:  108	 108	 108	 108	 108	   0	   0	   0	   0	   0
-partition: 0072	total: 540	per_label:    0	   0	   0	 108	 108	 108	   0	 108	   0	 108
-partition: 0073	total: 540	per_label:  108	 108	   0	   0	 108	   0	 108	   0	 108	   0
-partition: 0074	total: 540	per_label:  108	   0	 108	   0	 108	 108	   0	 108	   0	   0
-partition: 0075	total: 540	per_label:    0	   0	   0	 108	   0	 108	   0	 108	 108	 108
-partition: 0076	total: 540	per_label:  108	 108	 108	 108	   0	   0	   0	   0	 108	   0
-partition: 0077	total: 540	per_label:    0	   0	 108	   0	   0	 108	 108	   0	 108	 108
-partition: 0078	total: 540	per_label:  108	   0	   0	 108	   0	 108	   0	 108	   0	 108
-partition: 0079	total: 540	per_label:    0	 108	 108	   0	   0	 108	 108	   0	   0	 108
-partition: 0080	total: 540	per_label:  108	   0	   0	   0	 108	   0	 108	 108	   0	 108
-partition: 0081	total: 540	per_label:    0	 108	   0	 108	 108	   0	 108	   0	 108	   0
-partition: 0082	total: 540	per_label:    0	   0	 108	   0	 108	 108	 108	   0	   0	 108
-partition: 0083	total: 540	per_label:  108	   0	   0	 108	   0	   0	 108	 108	 108	   0
-partition: 0084	total: 540	per_label:  108	 108	 108	   0	   0	   0	 108	   0	 108	   0
-partition: 0085	total: 540	per_label:  108	   0	   0	 108	   0	 108	   0	 108	 108	   0
-partition: 0086	total: 540	per_label:    0	   0	 108	 108	 108	   0	 108	 108	   0	   0
-partition: 0087	total: 540	per_label:  108	   0	   0	   0	 108	 108	   0	 108	   0	 108
-partition: 0088	total: 540	per_label:    0	 108	   0	 108	   0	 108	 108	 108	   0	   0
-partition: 0089	total: 540	per_label:    0	 108	   0	 108	 108	 108	 108	   0	   0	   0
-partition: 0090	total: 540	per_label:  108	 108	   0	 108	 108	   0	   0	 108	   0	   0
-partition: 0091	total: 540	per_label:    0	   0	 108	 108	   0	 108	 108	   0	   0	 108
-partition: 0092	total: 540	per_label:  108	   0	 108	   0	   0	 108	 108	   0	   0	 108
-partition: 0093	total: 540	per_label:    0	   0	 108	 108	   0	 108	 108	   0	   0	 108
-partition: 0094	total: 540	per_label:  108	   0	 108	 108	 108	   0	   0	   0	   0	 108
-partition: 0095	total: 540	per_label:  108	   0	 108	 108	 108	   0	   0	   0	   0	 108
-partition: 0096	total: 540	per_label:  108	   0	   0	 108	 108	   0	 108	   0	   0	 108
-partition: 0097	total: 540	per_label:  108	 108	   0	 108	   0	   0	 108	   0	   0	 108
-partition: 0098	total: 540	per_label:  108	   0	   0	 108	   0	   0	 108	 108	   0	 108
-partition: 0099	total: 540	per_label:  108	 108	   0	   0	 108	   0	   0	 108	   0	 108
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-06cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-06cpp.txt
deleted file mode 100644
index 00a374ad3..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-06cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-06cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:    0	  90	  90	  90	  90	   0	   0	  90	   0	  90
-partition: 0001	total: 540	per_label:   90	  90	  90	  90	   0	  90	  90	   0	   0	   0
-partition: 0002	total: 540	per_label:    0	  90	  90	   0	  90	  90	  90	  90	   0	   0
-partition: 0003	total: 540	per_label:   90	   0	  90	   0	  90	  90	  90	   0	   0	  90
-partition: 0004	total: 540	per_label:    0	   0	  90	  90	  90	  90	  90	   0	   0	  90
-partition: 0005	total: 540	per_label:   90	  90	  90	   0	  90	   0	  90	   0	   0	  90
-partition: 0006	total: 540	per_label:   90	   0	   0	   0	  90	  90	   0	  90	  90	  90
-partition: 0007	total: 540	per_label:    0	   0	  90	  90	  90	  90	   0	  90	   0	  90
-partition: 0008	total: 540	per_label:   90	   0	   0	   0	  90	  90	  90	   0	  90	  90
-partition: 0009	total: 540	per_label:    0	  90	  90	   0	  90	  90	   0	   0	  90	  90
-partition: 0010	total: 540	per_label:   90	   0	  90	  90	  90	  90	   0	   0	  90	   0
-partition: 0011	total: 540	per_label:   90	   0	   0	  90	  90	  90	   0	  90	   0	  90
-partition: 0012	total: 540	per_label:    0	   0	  90	   0	  90	  90	  90	   0	  90	  90
-partition: 0013	total: 540	per_label:   90	  90	   0	  90	  90	   0	  90	  90	   0	   0
-partition: 0014	total: 540	per_label:    0	   0	  90	  90	  90	   0	   0	  90	  90	  90
-partition: 0015	total: 540	per_label:   90	  90	  90	  90	   0	   0	  90	   0	  90	   0
-partition: 0016	total: 540	per_label:    0	  90	  90	   0	   0	  90	  90	   0	  90	  90
-partition: 0017	total: 540	per_label:   90	  90	  90	   0	   0	   0	  90	   0	  90	  90
-partition: 0018	total: 540	per_label:    0	  90	  90	  90	   0	  90	   0	   0	  90	  90
-partition: 0019	total: 540	per_label:   90	  90	  90	   0	  90	  90	   0	   0	  90	   0
-partition: 0020	total: 540	per_label:   90	   0	   0	  90	  90	  90	   0	  90	  90	   0
-partition: 0021	total: 540	per_label:   90	  90	   0	  90	  90	  90	   0	   0	  90	   0
-partition: 0022	total: 540	per_label:    0	  90	   0	  90	   0	  90	  90	  90	  90	   0
-partition: 0023	total: 540	per_label:   90	  90	  90	  90	   0	  90	   0	  90	   0	   0
-partition: 0024	total: 540	per_label:    0	  90	  90	   0	  90	   0	  90	   0	  90	  90
-partition: 0025	total: 540	per_label:    0	  90	  90	   0	   0	  90	  90	  90	  90	   0
-partition: 0026	total: 540	per_label:   90	  90	   0	  90	  90	  90	   0	  90	   0	   0
-partition: 0027	total: 540	per_label:   90	  90	   0	  90	   0	   0	  90	  90	   0	  90
-partition: 0028	total: 540	per_label:    0	  90	  90	  90	  90	  90	   0	   0	  90	   0
-partition: 0029	total: 540	per_label:    0	   0	  90	  90	   0	  90	   0	  90	  90	  90
-partition: 0030	total: 540	per_label:    0	  90	  90	   0	  90	  90	   0	  90	  90	   0
-partition: 0031	total: 540	per_label:   90	   0	  90	   0	  90	   0	   0	  90	  90	  90
-partition: 0032	total: 540	per_label:   90	  90	  90	   0	   0	  90	  90	   0	  90	   0
-partition: 0033	total: 540	per_label:    0	   0	  90	  90	  90	  90	   0	  90	  90	   0
-partition: 0034	total: 540	per_label:   90	   0	   0	  90	  90	  90	  90	   0	  90	   0
-partition: 0035	total: 540	per_label:    0	   0	  90	  90	   0	  90	   0	  90	  90	  90
-partition: 0036	total: 540	per_label:   90	   0	  90	   0	  90	   0	   0	  90	  90	  90
-partition: 0037	total: 540	per_label:    0	  90	  90	  90	  90	  90	   0	   0	  90	   0
-partition: 0038	total: 540	per_label:   90	   0	  90	   0	  90	   0	  90	  90	  90	   0
-partition: 0039	total: 540	per_label:   90	   0	   0	  90	  90	  90	   0	  90	   0	  90
-partition: 0040	total: 540	per_label:   90	   0	  90	  90	   0	  90	  90	   0	   0	  90
-partition: 0041	total: 540	per_label:   90	  90	  90	  90	   0	   0	   0	   0	  90	  90
-partition: 0042	total: 540	per_label:    0	  90	  90	   0	   0	  90	  90	  90	  90	   0
-partition: 0043	total: 540	per_label:    0	   0	   0	  90	  90	  90	   0	  90	  90	  90
-partition: 0044	total: 540	per_label:    0	  90	   0	   0	  90	  90	  90	   0	  90	  90
-partition: 0045	total: 540	per_label:    0	  90	   0	  90	   0	   0	  90	  90	  90	  90
-partition: 0046	total: 540	per_label:   90	   0	  90	  90	   0	  90	  90	  90	   0	   0
-partition: 0047	total: 540	per_label:   90	  90	   0	  90	  90	   0	   0	  90	   0	  90
-partition: 0048	total: 540	per_label:   90	  90	   0	  90	   0	   0	  90	  90	   0	  90
-partition: 0049	total: 540	per_label:   90	  90	   0	   0	   0	   0	  90	  90	  90	  90
-partition: 0050	total: 540	per_label:   90	  90	  90	  90	   0	  90	   0	   0	   0	  90
-partition: 0051	total: 540	per_label:    0	   0	  90	   0	  90	   0	  90	  90	  90	  90
-partition: 0052	total: 540	per_label:   90	  90	   0	   0	  90	  90	   0	   0	  90	  90
-partition: 0053	total: 540	per_label:   90	   0	  90	   0	  90	  90	   0	  90	  90	   0
-partition: 0054	total: 540	per_label:    0	  90	  90	  90	   0	  90	  90	  90	   0	   0
-partition: 0055	total: 540	per_label:   90	  90	  90	   0	   0	  90	  90	   0	   0	  90
-partition: 0056	total: 540	per_label:   90	  90	   0	  90	   0	   0	  90	   0	  90	  90
-partition: 0057	total: 540	per_label:   90	  90	  90	   0	   0	   0	   0	  90	  90	  90
-partition: 0058	total: 540	per_label:    0	  90	   0	   0	  90	  90	  90	  90	  90	   0
-partition: 0059	total: 540	per_label:   90	  90	  90	   0	   0	   0	  90	  90	   0	  90
-partition: 0060	total: 540	per_label:    0	  90	  90	   0	  90	  90	   0	  90	  90	   0
-partition: 0061	total: 540	per_label:   90	  90	   0	   0	  90	  90	  90	  90	   0	   0
-partition: 0062	total: 540	per_label:    0	  90	  90	   0	  90	   0	   0	  90	  90	  90
-partition: 0063	total: 540	per_label:    0	  90	   0	  90	  90	   0	   0	  90	  90	  90
-partition: 0064	total: 540	per_label:   90	   0	   0	  90	  90	   0	  90	  90	   0	  90
-partition: 0065	total: 540	per_label:    0	  90	   0	   0	  90	  90	  90	  90	   0	  90
-partition: 0066	total: 540	per_label:   90	  90	  90	  90	  90	   0	   0	   0	   0	  90
-partition: 0067	total: 540	per_label:    0	  90	  90	  90	  90	  90	  90	   0	   0	   0
-partition: 0068	total: 540	per_label:    0	  90	  90	   0	   0	   0	  90	  90	  90	  90
-partition: 0069	total: 540	per_label:   90	  90	   0	  90	   0	   0	  90	  90	  90	   0
-partition: 0070	total: 540	per_label:    0	   0	   0	  90	  90	  90	  90	   0	  90	  90
-partition: 0071	total: 540	per_label:   90	  90	  90	  90	  90	  90	   0	   0	   0	   0
-partition: 0072	total: 540	per_label:   90	   0	   0	  90	  90	  90	   0	  90	   0	  90
-partition: 0073	total: 540	per_label:   90	   0	   0	  90	   0	   0	  90	  90	  90	  90
-partition: 0074	total: 540	per_label:    0	   0	  90	  90	  90	  90	  90	  90	   0	   0
-partition: 0075	total: 540	per_label:    0	  90	  90	  90	   0	  90	   0	  90	  90	   0
-partition: 0076	total: 540	per_label:   90	  90	  90	  90	   0	   0	   0	  90	  90	   0
-partition: 0077	total: 540	per_label:    0	   0	  90	   0	   0	  90	  90	  90	  90	  90
-partition: 0078	total: 540	per_label:   90	   0	   0	  90	  90	   0	  90	  90	   0	  90
-partition: 0079	total: 540	per_label:    0	  90	  90	   0	  90	  90	  90	   0	   0	  90
-partition: 0080	total: 540	per_label:   90	   0	   0	   0	  90	   0	  90	  90	  90	  90
-partition: 0081	total: 540	per_label:   90	  90	   0	   0	  90	   0	  90	   0	  90	  90
-partition: 0082	total: 540	per_label:   90	   0	  90	   0	  90	  90	  90	  90	   0	   0
-partition: 0083	total: 540	per_label:   90	   0	  90	  90	   0	   0	  90	  90	  90	   0
-partition: 0084	total: 540	per_label:    0	  90	  90	  90	   0	  90	  90	   0	  90	   0
-partition: 0085	total: 540	per_label:   90	   0	   0	  90	   0	  90	  90	  90	  90	   0
-partition: 0086	total: 540	per_label:    0	  90	  90	  90	  90	   0	  90	   0	   0	  90
-partition: 0087	total: 540	per_label:   90	   0	   0	   0	   0	  90	  90	  90	  90	  90
-partition: 0088	total: 540	per_label:   90	  90	   0	  90	   0	  90	  90	   0	   0	  90
-partition: 0089	total: 540	per_label:   90	  90	   0	  90	   0	  90	  90	  90	   0	   0
-partition: 0090	total: 540	per_label:    0	  90	  90	   0	  90	  90	  90	  90	   0	   0
-partition: 0091	total: 540	per_label:   90	  90	  90	  90	   0	   0	   0	  90	  90	   0
-partition: 0092	total: 540	per_label:   90	   0	  90	  90	   0	   0	  90	  90	  90	   0
-partition: 0093	total: 540	per_label:   90	  90	  90	  90	   0	   0	   0	   0	  90	  90
-partition: 0094	total: 540	per_label:   90	   0	   0	  90	  90	   0	  90	   0	  90	  90
-partition: 0095	total: 540	per_label:   90	   0	   0	  90	  90	   0	  90	   0	  90	  90
-partition: 0096	total: 540	per_label:   90	  90	   0	  90	  90	   0	  90	   0	   0	  90
-partition: 0097	total: 540	per_label:    0	  90	  90	  90	  90	   0	  90	   0	   0	  90
-partition: 0098	total: 540	per_label:   90	   0	   0	   0	  90	  90	  90	  90	   0	  90
-partition: 0099	total: 540	per_label:   90	   0	   0	  90	  90	  90	   0	  90	   0	  90
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-07cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-07cpp.txt
deleted file mode 100644
index d923a9719..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-07cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-07cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 539	per_label:   77	  77	  77	  77	  77	   0	   0	  77	   0	  77
-partition: 0001	total: 539	per_label:   77	  77	  77	  77	  77	  77	  77	   0	   0	   0
-partition: 0002	total: 539	per_label:    0	  77	  77	  77	  77	  77	  77	  77	   0	   0
-partition: 0003	total: 539	per_label:   77	   0	  77	   0	  77	  77	  77	   0	  77	  77
-partition: 0004	total: 539	per_label:    0	  77	  77	  77	   0	  77	  77	  77	   0	  77
-partition: 0005	total: 539	per_label:   77	  77	  77	  77	  77	   0	  77	  77	   0	   0
-partition: 0006	total: 539	per_label:   77	   0	  77	   0	  77	  77	   0	  77	  77	  77
-partition: 0007	total: 539	per_label:    0	   0	  77	  77	  77	  77	  77	  77	   0	  77
-partition: 0008	total: 539	per_label:    0	  77	   0	  77	  77	  77	  77	   0	  77	  77
-partition: 0009	total: 539	per_label:    0	  77	  77	   0	  77	  77	  77	   0	  77	  77
-partition: 0010	total: 539	per_label:   77	   0	  77	  77	  77	  77	   0	  77	  77	   0
-partition: 0011	total: 539	per_label:   77	   0	  77	  77	  77	  77	   0	  77	   0	  77
-partition: 0012	total: 539	per_label:    0	   0	  77	   0	  77	  77	  77	  77	  77	  77
-partition: 0013	total: 539	per_label:   77	  77	  77	  77	  77	   0	  77	  77	   0	   0
-partition: 0014	total: 539	per_label:    0	   0	  77	  77	  77	   0	  77	  77	  77	  77
-partition: 0015	total: 539	per_label:   77	  77	  77	  77	   0	   0	  77	   0	  77	  77
-partition: 0016	total: 539	per_label:   77	  77	   0	   0	   0	  77	  77	  77	  77	  77
-partition: 0017	total: 539	per_label:   77	  77	  77	   0	   0	   0	  77	  77	  77	  77
-partition: 0018	total: 539	per_label:   77	  77	  77	  77	   0	  77	   0	   0	  77	  77
-partition: 0019	total: 539	per_label:   77	  77	  77	   0	  77	  77	   0	   0	  77	  77
-partition: 0020	total: 539	per_label:   77	   0	   0	  77	  77	  77	  77	  77	  77	   0
-partition: 0021	total: 539	per_label:   77	  77	   0	  77	  77	  77	   0	  77	  77	   0
-partition: 0022	total: 539	per_label:    0	  77	   0	  77	   0	  77	  77	  77	  77	  77
-partition: 0023	total: 539	per_label:   77	  77	  77	  77	   0	  77	   0	  77	  77	   0
-partition: 0024	total: 539	per_label:   77	  77	  77	   0	  77	   0	  77	   0	  77	  77
-partition: 0025	total: 539	per_label:    0	  77	  77	   0	   0	  77	  77	  77	  77	  77
-partition: 0026	total: 539	per_label:   77	  77	   0	  77	  77	  77	   0	  77	  77	   0
-partition: 0027	total: 539	per_label:   77	  77	   0	  77	  77	   0	  77	  77	   0	  77
-partition: 0028	total: 539	per_label:    0	  77	  77	  77	  77	  77	   0	   0	  77	  77
-partition: 0029	total: 539	per_label:    0	  77	  77	  77	   0	  77	   0	  77	  77	  77
-partition: 0030	total: 539	per_label:    0	  77	  77	  77	  77	  77	   0	  77	  77	   0
-partition: 0031	total: 539	per_label:   77	  77	  77	   0	  77	   0	   0	  77	  77	  77
-partition: 0032	total: 539	per_label:   77	  77	  77	   0	  77	  77	  77	   0	  77	   0
-partition: 0033	total: 539	per_label:   77	   0	  77	  77	  77	  77	   0	  77	  77	   0
-partition: 0034	total: 539	per_label:   77	  77	   0	  77	   0	  77	  77	   0	  77	  77
-partition: 0035	total: 539	per_label:    0	   0	  77	  77	  77	  77	   0	  77	  77	  77
-partition: 0036	total: 539	per_label:   77	   0	  77	  77	  77	   0	   0	  77	  77	  77
-partition: 0037	total: 539	per_label:    0	  77	  77	  77	  77	  77	   0	   0	  77	  77
-partition: 0038	total: 539	per_label:   77	   0	  77	   0	  77	  77	  77	  77	  77	   0
-partition: 0039	total: 539	per_label:   77	   0	   0	  77	  77	  77	  77	  77	   0	  77
-partition: 0040	total: 539	per_label:   77	   0	  77	  77	   0	  77	  77	  77	   0	  77
-partition: 0041	total: 539	per_label:   77	  77	  77	  77	  77	   0	   0	   0	  77	  77
-partition: 0042	total: 539	per_label:   77	  77	  77	   0	   0	  77	  77	  77	  77	   0
-partition: 0043	total: 539	per_label:    0	   0	  77	  77	  77	  77	   0	  77	  77	  77
-partition: 0044	total: 539	per_label:    0	  77	   0	   0	  77	  77	  77	  77	  77	  77
-partition: 0045	total: 539	per_label:   77	  77	   0	  77	   0	   0	  77	  77	  77	  77
-partition: 0046	total: 539	per_label:   77	  77	  77	  77	   0	  77	  77	   0	   0	  77
-partition: 0047	total: 539	per_label:   77	  77	   0	  77	  77	   0	  77	  77	   0	  77
-partition: 0048	total: 539	per_label:   77	  77	  77	  77	   0	   0	  77	  77	   0	  77
-partition: 0049	total: 539	per_label:   77	  77	  77	   0	   0	   0	  77	  77	  77	  77
-partition: 0050	total: 539	per_label:   77	  77	  77	  77	  77	  77	   0	   0	   0	  77
-partition: 0051	total: 539	per_label:    0	   0	  77	  77	  77	   0	  77	  77	  77	  77
-partition: 0052	total: 539	per_label:   77	  77	   0	   0	  77	  77	  77	   0	  77	  77
-partition: 0053	total: 539	per_label:   77	   0	  77	  77	  77	  77	   0	  77	  77	   0
-partition: 0054	total: 539	per_label:   77	   0	  77	  77	   0	  77	  77	  77	   0	  77
-partition: 0055	total: 539	per_label:   77	  77	  77	   0	   0	  77	  77	  77	   0	  77
-partition: 0056	total: 539	per_label:   77	  77	   0	  77	   0	  77	  77	   0	  77	  77
-partition: 0057	total: 539	per_label:   77	  77	  77	   0	   0	   0	  77	  77	  77	  77
-partition: 0058	total: 539	per_label:    0	  77	   0	  77	  77	  77	  77	  77	  77	   0
-partition: 0059	total: 539	per_label:   77	  77	  77	  77	   0	  77	  77	  77	   0	   0
-partition: 0060	total: 539	per_label:    0	  77	  77	   0	  77	  77	   0	  77	  77	  77
-partition: 0061	total: 539	per_label:   77	  77	   0	   0	  77	  77	  77	  77	  77	   0
-partition: 0062	total: 539	per_label:    0	  77	  77	   0	  77	   0	  77	  77	  77	  77
-partition: 0063	total: 539	per_label:    0	  77	   0	  77	  77	  77	   0	  77	  77	  77
-partition: 0064	total: 539	per_label:   77	   0	   0	  77	  77	  77	  77	  77	   0	  77
-partition: 0065	total: 539	per_label:    0	  77	   0	  77	  77	  77	  77	  77	   0	  77
-partition: 0066	total: 539	per_label:   77	  77	  77	  77	  77	   0	   0	  77	   0	  77
-partition: 0067	total: 539	per_label:    0	  77	  77	  77	  77	  77	  77	  77	   0	   0
-partition: 0068	total: 539	per_label:   77	  77	  77	   0	   0	   0	  77	  77	  77	  77
-partition: 0069	total: 539	per_label:   77	  77	   0	  77	   0	  77	  77	  77	  77	   0
-partition: 0070	total: 539	per_label:    0	   0	  77	  77	  77	  77	  77	   0	  77	  77
-partition: 0071	total: 539	per_label:   77	  77	  77	  77	  77	  77	  77	   0	   0	   0
-partition: 0072	total: 539	per_label:   77	   0	   0	  77	  77	  77	   0	  77	  77	  77
-partition: 0073	total: 539	per_label:   77	  77	  77	   0	  77	   0	  77	  77	  77	   0
-partition: 0074	total: 539	per_label:   77	   0	  77	  77	  77	  77	   0	  77	   0	  77
-partition: 0075	total: 539	per_label:    0	  77	  77	  77	   0	  77	  77	  77	  77	   0
-partition: 0076	total: 539	per_label:   77	  77	  77	  77	  77	   0	   0	  77	  77	   0
-partition: 0077	total: 539	per_label:    0	   0	  77	   0	  77	  77	  77	  77	  77	  77
-partition: 0078	total: 539	per_label:   77	   0	   0	  77	   0	  77	  77	  77	  77	  77
-partition: 0079	total: 539	per_label:   77	  77	  77	   0	  77	  77	  77	   0	   0	  77
-partition: 0080	total: 539	per_label:   77	   0	  77	   0	  77	   0	  77	  77	  77	  77
-partition: 0081	total: 539	per_label:   77	  77	   0	  77	  77	  77	  77	   0	  77	   0
-partition: 0082	total: 539	per_label:   77	  77	  77	   0	  77	  77	  77	  77	   0	   0
-partition: 0083	total: 539	per_label:   77	   0	  77	  77	   0	  77	  77	  77	  77	   0
-partition: 0084	total: 539	per_label:    0	  77	  77	  77	   0	  77	  77	   0	  77	  77
-partition: 0085	total: 539	per_label:   77	   0	  77	  77	   0	  77	  77	   0	  77	  77
-partition: 0086	total: 539	per_label:    0	  77	  77	  77	  77	   0	  77	  77	   0	  77
-partition: 0087	total: 539	per_label:   77	   0	   0	   0	  77	  77	  77	  77	  77	  77
-partition: 0088	total: 539	per_label:   77	  77	   0	  77	   0	  77	  77	  77	   0	  77
-partition: 0089	total: 539	per_label:   77	  77	   0	  77	  77	  77	  77	  77	   0	   0
-partition: 0090	total: 539	per_label:    0	  77	  77	  77	  77	  77	   0	  77	   0	  77
-partition: 0091	total: 539	per_label:   77	  77	  77	  77	   0	  77	  77	   0	  77	   0
-partition: 0092	total: 539	per_label:   77	   0	  77	  77	  77	  77	  77	   0	  77	   0
-partition: 0093	total: 539	per_label:   77	  77	  77	  77	  77	   0	   0	   0	  77	  77
-partition: 0094	total: 539	per_label:    0	  77	  77	  77	  77	   0	  77	   0	  77	  77
-partition: 0095	total: 539	per_label:   77	  77	   0	  77	  77	   0	  77	   0	  77	  77
-partition: 0096	total: 539	per_label:   77	  77	   0	  77	  77	   0	  77	   0	  77	  77
-partition: 0097	total: 539	per_label:   77	  77	   0	  77	  77	   0	  77	   0	  77	  77
-partition: 0098	total: 539	per_label:   77	   0	   0	   0	  77	  77	  77	  77	  77	  77
-partition: 0099	total: 539	per_label:   77	  77	  77	   0	  77	   0	   0	  77	  77	  77
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-08cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-08cpp.txt
deleted file mode 100644
index 3307c1c28..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-08cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-08cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0001	total: 536	per_label:   67	  67	  67	  67	  67	  67	  67	   0	   0	  67
-partition: 0002	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	   0	  67
-partition: 0003	total: 536	per_label:   67	   0	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0004	total: 536	per_label:    0	  67	  67	  67	   0	  67	  67	  67	  67	  67
-partition: 0005	total: 536	per_label:   67	  67	  67	  67	  67	  67	  67	  67	   0	   0
-partition: 0006	total: 536	per_label:   67	  67	  67	   0	  67	  67	   0	  67	  67	  67
-partition: 0007	total: 536	per_label:   67	   0	  67	  67	  67	  67	  67	  67	   0	  67
-partition: 0008	total: 536	per_label:    0	  67	   0	  67	  67	  67	  67	  67	  67	  67
-partition: 0009	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0010	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0011	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0012	total: 536	per_label:    0	   0	  67	  67	  67	  67	  67	  67	  67	  67
-partition: 0013	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	  67	  67	   0
-partition: 0014	total: 536	per_label:   67	   0	  67	  67	  67	   0	  67	  67	  67	  67
-partition: 0015	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	   0	  67	  67
-partition: 0016	total: 536	per_label:   67	  67	  67	   0	  67	  67	  67	  67	  67	   0
-partition: 0017	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0018	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	   0	  67	  67
-partition: 0019	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	   0	  67	  67
-partition: 0020	total: 536	per_label:   67	   0	   0	  67	  67	  67	  67	  67	  67	  67
-partition: 0021	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	  67	  67	   0
-partition: 0022	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0023	total: 536	per_label:   67	  67	  67	  67	   0	  67	   0	  67	  67	  67
-partition: 0024	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0025	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0026	total: 536	per_label:   67	  67	   0	  67	  67	  67	   0	  67	  67	  67
-partition: 0027	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0028	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	   0	  67	  67
-partition: 0029	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	   0	  67	  67
-partition: 0030	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0031	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0032	total: 536	per_label:   67	  67	  67	   0	  67	  67	  67	   0	  67	  67
-partition: 0033	total: 536	per_label:   67	   0	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0034	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	  67	   0
-partition: 0035	total: 536	per_label:    0	   0	  67	  67	  67	  67	  67	  67	  67	  67
-partition: 0036	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0037	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	   0	  67	  67
-partition: 0038	total: 536	per_label:   67	   0	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0039	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0040	total: 536	per_label:   67	  67	  67	   0	   0	  67	  67	  67	  67	  67
-partition: 0041	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	   0	  67	  67
-partition: 0042	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0043	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0044	total: 536	per_label:   67	  67	   0	   0	  67	  67	  67	  67	  67	  67
-partition: 0045	total: 536	per_label:   67	  67	   0	  67	   0	  67	  67	  67	  67	  67
-partition: 0046	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0047	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	  67	   0	  67
-partition: 0048	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	   0	  67
-partition: 0049	total: 536	per_label:   67	   0	  67	   0	  67	  67	  67	  67	  67	  67
-partition: 0050	total: 536	per_label:   67	  67	  67	  67	  67	  67	   0	  67	   0	  67
-partition: 0051	total: 536	per_label:    0	  67	  67	  67	  67	   0	  67	  67	  67	  67
-partition: 0052	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	   0	  67	  67
-partition: 0053	total: 536	per_label:   67	   0	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0054	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	   0	  67
-partition: 0055	total: 536	per_label:   67	  67	  67	   0	   0	  67	  67	  67	  67	  67
-partition: 0056	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	   0	  67	  67
-partition: 0057	total: 536	per_label:   67	  67	  67	   0	   0	  67	  67	  67	  67	  67
-partition: 0058	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0059	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	  67	  67	   0
-partition: 0060	total: 536	per_label:   67	  67	  67	   0	  67	  67	   0	  67	  67	  67
-partition: 0061	total: 536	per_label:   67	  67	   0	   0	  67	  67	  67	  67	  67	  67
-partition: 0062	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0063	total: 536	per_label:    0	  67	  67	  67	  67	  67	   0	  67	  67	  67
-partition: 0064	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0065	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0066	total: 536	per_label:   67	  67	  67	  67	  67	   0	   0	  67	  67	  67
-partition: 0067	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0068	total: 536	per_label:    0	  67	  67	   0	  67	  67	  67	  67	  67	  67
-partition: 0069	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0070	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0071	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	   0	  67
-partition: 0072	total: 536	per_label:   67	  67	   0	  67	  67	  67	   0	  67	  67	  67
-partition: 0073	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0074	total: 536	per_label:   67	   0	  67	  67	  67	  67	  67	  67	  67	   0
-partition: 0075	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0076	total: 536	per_label:   67	  67	  67	  67	  67	   0	   0	  67	  67	  67
-partition: 0077	total: 536	per_label:    0	   0	  67	  67	  67	  67	  67	  67	  67	  67
-partition: 0078	total: 536	per_label:   67	  67	   0	  67	   0	  67	  67	  67	  67	  67
-partition: 0079	total: 536	per_label:   67	  67	  67	   0	  67	  67	  67	   0	  67	  67
-partition: 0080	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0081	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	  67	   0
-partition: 0082	total: 536	per_label:   67	  67	  67	   0	  67	  67	  67	  67	  67	   0
-partition: 0083	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0084	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	   0	  67	  67
-partition: 0085	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0086	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	  67	   0	  67
-partition: 0087	total: 536	per_label:   67	   0	   0	  67	  67	  67	  67	  67	  67	  67
-partition: 0088	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0089	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	  67	   0	  67
-partition: 0090	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	   0	  67
-partition: 0091	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	   0	  67
-partition: 0092	total: 536	per_label:   67	  67	  67	  67	   0	  67	  67	  67	  67	   0
-partition: 0093	total: 536	per_label:   67	  67	  67	   0	  67	   0	  67	  67	  67	  67
-partition: 0094	total: 536	per_label:    0	  67	  67	  67	  67	  67	  67	  67	   0	  67
-partition: 0095	total: 536	per_label:   67	  67	   0	  67	  67	  67	  67	   0	  67	  67
-partition: 0096	total: 536	per_label:   67	   0	  67	  67	  67	   0	  67	  67	  67	  67
-partition: 0097	total: 536	per_label:   67	  67	  67	  67	  67	   0	  67	   0	  67	  67
-partition: 0098	total: 536	per_label:   67	   0	  67	  67	   0	  67	  67	  67	  67	  67
-partition: 0099	total: 536	per_label:   67	  67	  67	  67	   0	   0	  67	  67	  67	  67
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-09cpp.txt b/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-09cpp.txt
deleted file mode 100644
index 882fb7a6c..000000000
--- a/xain_fl/datasets/stats/datasets/fashion-mnist-100p-noniid-09cpp.txt
+++ /dev/null
@@ -1,106 +0,0 @@
-
-name: fashion-mnist-100p-noniid-09cpp
-number_of_examples_per_label_per_shard
-partition: 0000	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0001	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0002	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0003	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0004	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0005	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0006	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0007	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0008	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0009	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0010	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0011	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0012	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0013	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0014	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0015	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0016	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0017	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0018	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0019	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0020	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0021	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0022	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0023	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0024	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0025	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0026	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0027	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0028	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0029	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0030	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0031	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0032	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0033	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0034	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0035	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0036	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0037	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0038	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0039	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0040	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0041	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0042	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0043	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0044	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0045	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0046	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0047	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0048	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0049	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0050	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0051	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0052	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0053	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0054	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0055	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0056	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0057	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0058	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0059	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0060	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0061	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0062	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0063	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0064	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0065	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0066	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0067	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0068	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0069	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0070	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0071	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0072	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: 0073	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0074	total: 540	per_label:   60	   0	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0075	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0076	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0077	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0078	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0079	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0080	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0081	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0082	total: 540	per_label:   60	  60	  60	   0	  60	  60	  60	  60	  60	  60
-partition: 0083	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0084	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0085	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0086	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0087	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0088	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0089	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0090	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	   0	  60
-partition: 0091	total: 540	per_label:   60	  60	   0	  60	  60	  60	  60	  60	  60	  60
-partition: 0092	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	  60	  60	   0
-partition: 0093	total: 540	per_label:   60	  60	  60	  60	  60	   0	  60	  60	  60	  60
-partition: 0094	total: 540	per_label:    0	  60	  60	  60	  60	  60	  60	  60	  60	  60
-partition: 0095	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0096	total: 540	per_label:   60	  60	  60	  60	   0	  60	  60	  60	  60	  60
-partition: 0097	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0098	total: 540	per_label:   60	  60	  60	  60	  60	  60	  60	   0	  60	  60
-partition: 0099	total: 540	per_label:   60	  60	  60	  60	  60	  60	   0	  60	  60	  60
-partition: val	total: 6000	per_label:  600	 600	 600	 600	 600	 600	 600	 600	 600	 600
-partition: test	total: 10000	per_label: 1000	1000	1000	1000	1000	1000	1000	1000	1000	1000
-========================================================================================================================
diff --git a/xain_fl/datasets/stats/stats.py b/xain_fl/datasets/stats/stats.py
deleted file mode 100644
index f0bc48bc4..000000000
--- a/xain_fl/datasets/stats/stats.py
+++ /dev/null
@@ -1,91 +0,0 @@
-from typing import Dict, List
-
-import numpy as np
-
-from xain_fl.types import FederatedDataset, Partition
-
-PartitionStat = Dict[str, List[int]]
-
-
-class DSStats:
-    def __init__(self, name: str, ds: FederatedDataset):
-        self.name = name
-        self.ds = ds
-
-    def __repr__(self) -> str:
-        width = 120
-        line = "=" * width + "\n"
-        output = "\nname: {}\n".format(self.name)
-
-        all_stats = self.all()
-
-        topic = "number_of_examples_per_label_per_shard"
-        stat = all_stats[topic]
-
-        output += "{}\n".format(topic)
-
-        for part_index, part in stat.items():
-            output += "partition: {}\t".format(part_index)
-            output += "total: {}\t".format(part["total"])
-            output += "per_label: {}".format(
-                "\t".join([str(v).rjust(4) for v in part["per_label"]])
-            )
-            output += "\n"
-
-        output += line
-
-        return output
-
-    def all(self) -> Dict[str, Dict[str, PartitionStat]]:
-        stats = {}
-
-        stats[
-            "number_of_examples_per_label_per_shard"
-        ] = self.number_of_examples_per_label_per_shard()
-
-        return stats
-
-    def number_of_examples_per_label_per_shard(self) -> Dict[str, PartitionStat]:
-        xy_partitions, xy_val, xy_test = self.ds
-
-        stats = {}
-
-        zfill_width = int(np.log(len(xy_partitions)))
-
-        ys = [y for (_, y) in xy_partitions]
-        all_labels = np.unique(np.concatenate(ys, axis=0))
-
-        for index, xy_par in enumerate(xy_partitions):
-            key = str(index).zfill(zfill_width)
-
-            stats[key] = self.number_of_examples_per_label(
-                xy=xy_par, possible_labels=all_labels
-            )
-
-        stats["val"] = self.number_of_examples_per_label(
-            xy=xy_val, possible_labels=all_labels
-        )
-        stats["test"] = self.number_of_examples_per_label(
-            xy=xy_test, possible_labels=all_labels
-        )
-
-        return stats
-
-    @staticmethod
-    def number_of_examples_per_label(
-        xy: Partition, possible_labels: List
-    ) -> PartitionStat:
-        x, y = xy
-
-        possible_labels = list(possible_labels)
-        per_label_counts = [0] * len(possible_labels)
-
-        assert x.shape[0] == y.shape[0], "Number of examples and labels don't match"
-
-        [unique_labels, unique_counts] = np.unique(y, return_counts=True)
-
-        for i, l in enumerate(unique_labels):
-            per_label_counts_index = possible_labels.index(l)
-            per_label_counts[per_label_counts_index] = unique_counts[i]
-
-        return {"total": x.shape[0], "per_label": per_label_counts}
diff --git a/xain_fl/datasets/stats/stats_test.py b/xain_fl/datasets/stats/stats_test.py
deleted file mode 100644
index 5e4eb3ef9..000000000
--- a/xain_fl/datasets/stats/stats_test.py
+++ /dev/null
@@ -1,24 +0,0 @@
-from .stats import DSStats
-
-
-def test_DSStats_all(mock_federated_dataset):
-    ds_stats = DSStats("fed_ds", mock_federated_dataset)
-
-    stats = ds_stats.all()
-
-    assert isinstance(stats, dict)
-    assert isinstance(stats["number_of_examples_per_label_per_shard"], dict)
-
-    for key, stat in stats["number_of_examples_per_label_per_shard"].items():
-        assert "total" in stat
-        assert "per_label" in stat
-
-        if key == "val":
-            assert stat["total"] == 60
-        elif key == "test":
-            assert stat["total"] == 100
-        else:
-            assert stat["total"] == 270
-
-        assert isinstance(stat["per_label"], list)
-        assert len(stat["per_label"]) == 10
diff --git a/xain_fl/datasets/storage.py b/xain_fl/datasets/storage.py
deleted file mode 100644
index 6f85a8cea..000000000
--- a/xain_fl/datasets/storage.py
+++ /dev/null
@@ -1,203 +0,0 @@
-import concurrent.futures
-import os
-from typing import Tuple
-
-import numpy as np
-import requests
-from absl import flags
-
-from xain_fl.helpers.sha1 import checksum
-from xain_fl.logger import get_logger
-from xain_fl.types import FederatedDataset, Partition
-
-from . import hashes
-
-FLAGS = flags.FLAGS
-
-
-logger = get_logger(__name__)
-
-
-def default_get_local_datasets_dir():
-    """Returns absolute path to datasets directory
-
-    Returns
-        str: absolute datasets directory path
-    """
-    return FLAGS.local_datasets_dir
-
-
-def get_dataset_dir(dataset_name: str, local_datasets_dir: str) -> str:
-    """Will return dataset directory and create it if its not already present
-
-    Args:
-        dataset_name (str)
-        local_datasets_dir (str)
-
-    Returns:
-        str: Absolute directory path to newly created datasets
-            directory in local datasets dir
-    """
-    dataset_dir = os.path.join(local_datasets_dir, dataset_name)
-
-    if not os.path.isdir(dataset_dir):
-        os.makedirs(dataset_dir, exist_ok=True)
-
-    return dataset_dir
-
-
-def fetch_ndarray(url, fpath):
-    """Downloads file from url and store at fpath
-
-    Args:
-        url (str): URL from which to download the ndarray file
-        fpath (str): Absolute path under which to store the file
-    """
-    r = requests.get(url, stream=True)
-
-    logger.info("Fetching file from url", url=url)
-
-    if r.status_code != 200:
-        raise Exception("Received HTTP Status {} for url {}".format(r.status_code, url))
-
-    handle = open(fpath, "wb")
-    for chunk in r.iter_content(chunk_size=1024):
-        if chunk:  # filter out keep-alive new chunks
-            handle.write(chunk)
-
-
-def load_ndarray(
-    dataset_name: str, ndarray_name: str, ndarray_hash: str, local_datasets_dir: str
-):
-    """Downloads dataset ndarray and loads from disk if already present
-
-    Args:
-        datasets_repository (str): datasets repository base URL
-        dataset_name (str): Name of dataset in repository
-        ndarray_name (str): ndarray name. Example: "x_00.npy"
-        local_datasets_dir (str): Directory in which all local datasets are stored
-        cleanup (bool): Cleanup file if it has the wrong hash
-
-    Returns:
-        np.ndarray: Loaded numpy ndarray
-    """
-    url = "{}/{}/{}".format(FLAGS.datasets_repository, dataset_name, ndarray_name)
-
-    dataset_dir = get_dataset_dir(dataset_name, local_datasets_dir)
-    fpath = os.path.join(dataset_dir, ndarray_name)
-
-    if FLAGS.fetch_datasets and not os.path.isfile(fpath):
-        fetch_ndarray(url, fpath)
-
-    # Check sha1 checksum after conditional fetch even when no download
-    # occured and local dataset was used to avoid accidental corruption
-    sha1 = checksum(fpath)
-
-    if sha1 != ndarray_hash:
-        # Delete the downloaded file if it has the wrong hash
-        # Otherwise the next invocation will not download it again
-        # which is not a desired behavior
-        os.remove(fpath)
-
-        raise Exception(
-            "Given hash {} for file {} does not match".format(
-                ndarray_hash, ndarray_name
-            )
-        )
-
-    ndarray = np.load(fpath)
-
-    return ndarray
-
-
-def load_split(
-    dataset_name: str,
-    split_id: str,
-    split_hashes: Tuple[str, str],
-    local_datasets_dir=str,
-) -> Partition:
-    """Downloads dataset partition and loads from disk if already present
-
-    Args:
-        dataset_name (str): Name of dataset in dataset repository
-        split_id (str): ndarray name. Example: "x_00.npy"
-        split_hashes (str, str): Tuple of hashes of x and y ndarray
-        local_datasets_dir (bool): Directory in which all local datasets are stored
-
-    Returns:
-        Partition: Federated Dataset Partition
-    """
-    x_name = "x_{}.npy".format(split_id)
-    x_hash = split_hashes[0]
-
-    y_name = "y_{}.npy".format(split_id)
-    y_hash = split_hashes[1]
-
-    x = load_ndarray(
-        dataset_name=dataset_name,
-        ndarray_name=x_name,
-        ndarray_hash=x_hash,
-        local_datasets_dir=local_datasets_dir,
-    )
-
-    y = load_ndarray(
-        dataset_name=dataset_name,
-        ndarray_name=y_name,
-        ndarray_hash=y_hash,
-        local_datasets_dir=local_datasets_dir,
-    )
-
-    return x, y
-
-
-def load_splits(
-    dataset_name: str, get_local_datasets_dir=default_get_local_datasets_dir
-) -> FederatedDataset:
-    """Loads FederatedDataset from local datasets directory and from remote
-    datasets repository if not locally present
-
-    Args:
-        dataset_name (str): Name of dataset in repository
-        get_local_datasets_dir (Callable): Function which returns an absolute
-            path to datasets directory
-
-    Returns:
-        FederatedDataset
-    """
-
-    xy_splits = []
-    xy_val = (None, None)
-    xy_test = (None, None)
-
-    dataset_split_hashes = hashes.datasets[dataset_name]
-
-    local_datasets_dir = get_local_datasets_dir()
-
-    def load_method(split_id: str):
-        data = load_split(
-            dataset_name=dataset_name,
-            split_id=split_id,
-            # passing respective hash tuple for given split_id
-            split_hashes=dataset_split_hashes[split_id],
-            local_datasets_dir=local_datasets_dir,
-        )
-
-        return split_id, data
-
-    with concurrent.futures.ThreadPoolExecutor() as executor:
-        future_results = [
-            executor.submit(load_method, split_id) for split_id in dataset_split_hashes
-        ]
-        concurrent.futures.wait(future_results)
-
-    for future in future_results:
-        split_id, data = future.result()
-
-        if split_id == "test":
-            xy_test = data
-        elif split_id == "val":
-            xy_val = data
-        else:
-            xy_splits.append(data)
-
-    return xy_splits, xy_val, xy_test
diff --git a/xain_fl/datasets/storage_test.py b/xain_fl/datasets/storage_test.py
deleted file mode 100644
index c2a780ddc..000000000
--- a/xain_fl/datasets/storage_test.py
+++ /dev/null
@@ -1,20 +0,0 @@
-import pytest
-
-from . import storage
-
-
-@pytest.mark.integration
-def test_load_ndarray_wrong_hash(tmp_path):
-    # Prepare
-    dataset_name = "integration_test"
-    ndarray_name = "x_00.npy"
-    ndarray_hash = "wrong_hash"
-
-    # Execute and expect to fail
-    with pytest.raises(Exception):
-        storage.load_ndarray(
-            dataset_name=dataset_name,
-            ndarray_name=ndarray_name,
-            ndarray_hash=ndarray_hash,
-            local_datasets_dir=tmp_path,
-        )
diff --git a/xain_fl/datasets/testing.py b/xain_fl/datasets/testing.py
deleted file mode 100644
index 64d2a850f..000000000
--- a/xain_fl/datasets/testing.py
+++ /dev/null
@@ -1,96 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-from tensorflow.data import Dataset
-
-from xain_fl.types import FederatedDataset, KerasDataset
-
-
-def load(keras_dataset: Dataset) -> KerasDataset:
-    """Loads Keras Dataset in predictable form
-
-    Args:
-        keras_dataset (Dataset)
-
-    Returns:
-        Dataset
-    """
-    (x_train, y_train), (x_test, y_test) = keras_dataset.load_data()
-
-    y_train = y_train.reshape((y_train.shape[0],))
-    y_test = y_test.reshape((y_test.shape[0],))
-
-    return (x_train, y_train), (x_test, y_test)
-
-
-def unpack_keras_and_federated_dataset(
-    keras_dataset: Dataset, federated_dataset: FederatedDataset
-) -> Tuple[Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray]]:
-    """Unpacks Keras Dataset and Federated Dataset
-
-    Args:
-        keras_dataset (Dataset)
-        federated_dataset (FederatedDataset)
-
-    Returns:
-        (np.ndarray, np.ndarray), (np.ndarray, np.ndarray)
-    """
-    # Unpack and group each dataset
-    (x1_keras, y1_keras), (x2_keras, y2_keras) = keras_dataset
-    xy1_fed_splits, (x2_fed, y2_fed), (x3_fed, y3_fed) = federated_dataset
-    x1_fed_splits, y1_fed_splits = zip(*xy1_fed_splits)
-
-    x_keras = np.concatenate([x1_keras, x2_keras], axis=0)
-    y_keras = np.concatenate([y1_keras, y2_keras], axis=0)
-    assert x_keras.shape[0] == y_keras.shape[0]
-
-    x_fed = np.concatenate([*x1_fed_splits, x2_fed, x3_fed])
-    y_fed = np.concatenate([*y1_fed_splits, y2_fed, y3_fed])
-    assert x_fed.shape[0] == y_fed.shape[0]
-
-    # Shapes of unpacked and grouped datasets should be same
-    assert x_keras.shape == x_fed.shape
-    assert y_keras.shape == y_fed.shape
-
-    return (x_keras, y_keras), (x_fed, y_fed)
-
-
-def hash_xy(x, y):
-    """Produces unique hash for given x and y
-
-    Args:
-        x (np.ndarray)
-        y (np.ndarray)
-
-    Returns
-        str: Hash of combination of x and y
-    """
-    return hash(x.tobytes() + y.tobytes())
-
-
-def assert_dataset_origin(keras_dataset, federated_dataset):
-    """Asserts that given Federated Dataset is derived from given
-    Keras Dataset. Function will throw if otherwise.
-
-    Args:
-        keras_dataset (Dataset)
-        federated_dataset (FederatedDataset)
-    """
-    # Unpack and group each dataset
-    (x_keras, y_keras), (x_fed, y_fed) = unpack_keras_and_federated_dataset(
-        keras_dataset, federated_dataset
-    )
-
-    assert x_keras.shape == x_fed.shape
-    assert y_keras.shape == y_fed.shape
-
-    hash_table = {hash_xy(x, y): 0 for (x, y) in zip(x_keras, y_keras)}
-
-    for (x, y) in zip(x_fed, y_fed):
-        hash_table[hash_xy(x, y)] += 1
-
-    counts = list(hash_table.values())
-    unq = np.unique(counts)
-
-    assert len(unq) == 1, "Some examples are duplicate or not existing in keras dataset"
-    assert unq[0] == 1, "Federated example not found in original keras dataset"
diff --git a/xain_fl/datasets/testing_test.py b/xain_fl/datasets/testing_test.py
deleted file mode 100644
index 475e0ee20..000000000
--- a/xain_fl/datasets/testing_test.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import pytest
-
-from . import testing
-
-
-def test_assert_dataset_origin(
-    mock_simple_keras_dataset, mock_simple_federated_dataset
-):
-    # Execute & Assert
-    testing.assert_dataset_origin(
-        keras_dataset=mock_simple_keras_dataset,
-        federated_dataset=mock_simple_federated_dataset,
-    )
-
-
-def test_assert_dataset_origin_raise(
-    mock_simple_keras_dataset, mock_simple_federated_dataset
-):
-    # Prepare
-    # Get reference to x_train
-    (x_train, _), _ = mock_simple_keras_dataset
-
-    # And change one number in the first colum of the first example
-    x_train[0][0][0] += 1
-
-    # Execute & Assert
-    with pytest.raises(Exception):
-        testing.assert_dataset_origin(
-            keras_dataset=mock_simple_keras_dataset,
-            federated_dataset=mock_simple_federated_dataset,
-        )
diff --git a/xain_fl/fl/__init__.py b/xain_fl/fl/__init__.py
index 6e501026a..1c5687e11 100644
--- a/xain_fl/fl/__init__.py
+++ b/xain_fl/fl/__init__.py
@@ -2,6 +2,4 @@
 It consists the following subpackages:
 
     - `coordinator`
-    - `participant`
-    - `logging`
 """
diff --git a/xain_fl/fl/coordinator/__init__.py b/xain_fl/fl/coordinator/__init__.py
index f122cfa79..dfbfbed56 100644
--- a/xain_fl/fl/coordinator/__init__.py
+++ b/xain_fl/fl/coordinator/__init__.py
@@ -1,12 +1,9 @@
 """Contains coordinator logic which runs the central server in federated
 learning. It consists of the following modules:
 
-    - coordinator
     - aggregate
     - controller
-    - evaluator
 """
 
 from xain_fl.fl.coordinator.aggregate import Aggregator
 from xain_fl.fl.coordinator.controller import RandomController
-from xain_fl.fl.coordinator.coordinator import Coordinator
diff --git a/xain_fl/fl/coordinator/aggregate.py b/xain_fl/fl/coordinator/aggregate.py
index da5ff7853..8f41d6f99 100644
--- a/xain_fl/fl/coordinator/aggregate.py
+++ b/xain_fl/fl/coordinator/aggregate.py
@@ -6,21 +6,22 @@
 
 import numpy as np
 
-from xain_fl.logger import get_logger
-from xain_fl.types import Theta
+from xain_fl.fl.types import Theta
+from xain_fl.logger import StructLogger, get_logger
 
-from .evaluator import Evaluator
+logger: StructLogger = get_logger(__name__)
 
-logger = get_logger(__name__)
 
-
-class Aggregator(ABC):
+class Aggregator(ABC):  # pylint: disable=too-few-public-methods
     """Abstract base class which provides an interface to the coordinator that
     enables different aggregation implementations.
     """
 
     def __init__(self):
-        pass
+        """[summary]
+
+        .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+        """
 
     @abstractmethod
     def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
@@ -36,7 +37,7 @@ def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
         raise NotImplementedError()
 
 
-class IdentityAgg(Aggregator):
+class IdentityAgg(Aggregator):  # pylint: disable=too-few-public-methods
     """Provides identity aggregation, i.e. the aggregate method expects
     a list containing a single element and returns that element.
     """
@@ -47,25 +48,28 @@ def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
         return thetas[0][0]
 
 
-class FederatedAveragingAgg(Aggregator):
-    """Provides federated averaging aggregation, i.e. a weighted average."""
+class ModelSumAgg(Aggregator):  # pylint: disable=too-few-public-methods
+    """Provides a sum-of-models aggregation."""
 
     def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
-        theta_list = [theta for theta, _ in thetas]
-        weighting = np.array([num_examples for _, num_examples in thetas])
-        return federated_averaging(theta_list, weighting)
+        """Aggregates a given list of models by summation.
 
+        Args:
+            thetas (~typing.List[~xain_fl.fl.types.Theta]): List of thetas.
+
+        Returns:
+            ~xain_fl.fl.types.Theta: The aggregated model weights.
+        """
+        return [sum(th) for th, _ in thetas]
 
-class EvoAgg(Aggregator):
-    """Experimental"""
 
-    def __init__(self, evaluator: Evaluator):
-        super().__init__()
-        self.evaluator = evaluator
+class FederatedAveragingAgg(Aggregator):  # pylint: disable=too-few-public-methods
+    """Provides federated averaging aggregation, i.e. a weighted average."""
 
     def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
-        weight_matrices = [theta for theta, num_examples in thetas]
-        return evo_agg(weight_matrices, self.evaluator)
+        theta_list = [theta for theta, _ in thetas]
+        weighting = np.array([num_examples for _, num_examples in thetas])
+        return federated_averaging(theta_list, weighting)
 
 
 def federated_averaging(thetas: List[Theta], weighting: np.ndarray) -> Theta:
@@ -84,59 +88,71 @@ def federated_averaging(thetas: List[Theta], weighting: np.ndarray) -> Theta:
     assert len(thetas) == weighting.shape[0]
 
     theta_avg: Theta = thetas[0]
-    for w in theta_avg:
-        w *= weighting[0]
+    for weights in theta_avg:
+        weights *= weighting[0]
 
     # Aggregate (weighted) updates
     for theta, update_weighting in zip(thetas[1:], weighting[1:]):
-        for w_index, w in enumerate(theta):
-            theta_avg[w_index] += update_weighting * w
+        for w_index, weights in enumerate(theta):
+            theta_avg[w_index] += update_weighting * weights
 
     weighting_sum = np.sum(weighting)
-    for w in theta_avg:
-        w /= weighting_sum
+    for weights in theta_avg:
+        weights /= weighting_sum
 
     return theta_avg
 
 
-def evo_agg(thetas: List[Theta], evaluator: Evaluator) -> Theta:
-    """Experimental
+# TODO: (XP-351) decide how to continue with that
+# class EvoAgg(Aggregator):
+#     """Experimental"""
 
-        - Init different weightings
-        - Aggregate thetas according to those weightings ("candidates")
-        - Evaluate all candidates on the validation set
-        - Pick (a) best candidate, or (b) average of n best candidates
-    """
-    # Compute candidates
-    # TODO in parallel, do:
-    theta_prime_candidates = []
-    for _ in range(3):
-        candidate = _compute_candidate(thetas, evaluator)
-        logger.debug("Candidate metadata", weighting=candidate[0], loss=candidate[2])
-
-        theta_prime_candidates.append(candidate)
-    # Return best candidate
-    best_candidate = _pick_best_candidate(theta_prime_candidates)
-    return best_candidate
-
-
-def _pick_best_candidate(candidates: List) -> Theta:
-    _, best_candidate, best_loss, _ = candidates[0]
-    for _, candidate, loss, _ in candidates[1:]:
-        if loss < best_loss:
-            best_candidate = candidate
-            best_loss = loss
-    return best_candidate
-
-
-def _compute_candidate(
-    thetas: Theta, evaluator: Evaluator
-) -> Tuple[np.ndarray, Theta, float, float]:
-    weighting = _random_weighting(len(thetas))
-    theta_prime_candidate = federated_averaging(thetas, weighting)
-    loss, acc = evaluator.evaluate(theta_prime_candidate)
-    return weighting, theta_prime_candidate, loss, acc
-
-
-def _random_weighting(num_weightings: int, low=0.5, high=1.5) -> np.ndarray:
-    return np.random.uniform(low=low, high=high, size=(num_weightings,))
+#     def __init__(self, evaluator: Evaluator):
+#         super().__init__()
+#         self.evaluator = evaluator
+
+#     def aggregate(self, thetas: List[Tuple[Theta, int]]) -> Theta:
+#         weight_matrices = [theta for theta, num_examples in thetas]
+#         return evo_agg(weight_matrices, self.evaluator)
+
+# def evo_agg(thetas: List[Theta], evaluator: Evaluator) -> Theta:
+#     """Experimental
+
+#         - Init different weightings
+#         - Aggregate thetas according to those weightings ("candidates")
+#         - Evaluate all candidates on the validation set
+#         - Pick (a) best candidate, or (b) average of n best candidates
+#     """
+#     # Compute candidates
+#     # TODO in parallel, do:
+#     theta_prime_candidates = []
+#     for _ in range(3):
+#         candidate = _compute_candidate(thetas, evaluator)
+#         logger.debug("Candidate metadata", weighting=candidate[0], loss=candidate[2])
+
+#         theta_prime_candidates.append(candidate)
+#     # Return best candidate
+#     best_candidate = _pick_best_candidate(theta_prime_candidates)
+#     return best_candidate
+
+
+# def _pick_best_candidate(candidates: List) -> Theta:
+#     _, best_candidate, best_loss, _ = candidates[0]
+#     for _, candidate, loss, _ in candidates[1:]:
+#         if loss < best_loss:
+#             best_candidate = candidate
+#             best_loss = loss
+#     return best_candidate
+
+
+# def _compute_candidate(
+#     thetas: Theta, evaluator: Evaluator
+# ) -> Tuple[np.ndarray, Theta, float, float]:
+#     weighting = _random_weighting(len(thetas))
+#     theta_prime_candidate = federated_averaging(thetas, weighting)
+#     loss, acc = evaluator.evaluate(theta_prime_candidate)
+#     return weighting, theta_prime_candidate, loss, acc
+
+
+# def _random_weighting(num_weightings: int, low=0.5, high=1.5) -> np.ndarray:
+#     return np.random.uniform(low=low, high=high, size=(num_weightings,))
diff --git a/xain_fl/fl/coordinator/controller.py b/xain_fl/fl/coordinator/controller.py
index 0ac571cac..470b88679 100644
--- a/xain_fl/fl/coordinator/controller.py
+++ b/xain_fl/fl/coordinator/controller.py
@@ -1,62 +1,103 @@
-"""Provides an abstract base class Controller and multiple sub-classes
-such as CycleRandomController.
-"""
+"""Provides an abstract base class Controller and the RandomController
+currently used by the Coordinator."""
+
 from abc import ABC, abstractmethod
 from typing import List
 
 import numpy as np
 
 
+# TODO: raise exceptions for invalid attribute values: https://xainag.atlassian.net/browse/XP-387
 class Controller(ABC):
     """Abstract base class which provides an interface to the coordinator that
     enables different selection strategies.
 
     Attributes:
-        participant_ids (:obj:`list` of :obj:`str`): The list of IDs of the
-            all the available participants, a subset of which will be selected.
         fraction_of_participants (:obj:`float`, optional): The fraction of total
-            participant ids to be selected. Defaults to 1.0, meaning that
-            all participant ids will be selected.
+            participant IDs to be selected. Defaults to 1.0, meaning that
+            all participant IDs will be selected. It must be in the (0.0, 1.0] interval.
     """
 
-    def __init__(
-        self, participant_ids: List[str], fraction_of_participants: float = 1.0
-    ) -> None:
-        self.participant_ids: List[str] = participant_ids
+    def __init__(self, fraction_of_participants: float = 1.0) -> None:
+        """[summary]
+
+        .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+        Args:
+            fraction_of_participants (float): [description]. Defaults to 1.0.
+        """
+
         self.fraction_of_participants: float = fraction_of_participants
-        self.num_ids_to_select: int = self.get_num_ids_to_select()
 
-    def get_num_ids_to_select(self) -> int:
-        """Calculates how many participant ids need to be selected.
+    def get_num_ids_to_select(self, len_participant_ids: int) -> int:
+        """Calculates how many participant IDs need to be selected.
+
+        Args:
+            len_participant_ids (:obj:`int`): The length of the list of IDs of all the
+                available participants.
 
         Returns:
-            :obj:`int`: Number of participant ids to be selected
+            :obj:`int`: Number of participant IDs to be selected
         """
-        raw_num_ids_to_select = (
-            len(self.participant_ids) * self.fraction_of_participants
-        )
+        raw_num_ids_to_select = len_participant_ids * self.fraction_of_participants
         max_valid_value = max(1, np.ceil(raw_num_ids_to_select))
-        minimum_valid_value = min(len(self.participant_ids), max_valid_value)
+        minimum_valid_value = min(len_participant_ids, max_valid_value)
         return int(minimum_valid_value)
 
     @abstractmethod
-    def select_ids(self) -> List[str]:
-        """Returns the selected indices of next round
+    def select_ids(self, participant_ids: List[str]) -> List[str]:
+        """Returns the selected indices of next round.
+
+        Args:
+            participant_ids (:obj:`list` of :obj:`str`): The list of IDs of all the
+                available participants, a subset of which will be selected.
 
         Returns:
-            :obj:`list` of :obj:`str`: Unordered list of selected ids
+            :obj:`list` of :obj:`str`: List of selected participant IDs
         """
         raise NotImplementedError("not implemented")
 
 
+class IdController(Controller):
+    """[summary
+
+    ... todo: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    def select_ids(self, participant_ids: List[str]) -> List[str]:
+        """Selects all given participants.
+
+        Args:
+            participant_ids (:obj:`list` of :obj:`str`): The list of IDs of all the
+                available participants, a subset of which will be selected.
+
+        Returns:
+            :obj:`list` of :obj:`str`: List of selected participant IDs
+        """
+
+        return participant_ids
+
+
 class RandomController(Controller):
-    def select_ids(self) -> List[str]:
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+    Args:
+        Controller ([type]): [description]
+    """
+
+    def select_ids(self, participant_ids: List[str]) -> List[str]:
         """Randomly samples self.num_ids_to_select from the population of participants_ids,
         without replacement.
 
+        Args:
+            participant_ids (:obj:`list` of :obj:`str`): The list of IDs of all the
+                available participants, a subset of which will be selected.
+
         Returns:
-            :obj:`list` of :obj:`str`: List of selected participant ID's
+            :obj:`list` of :obj:`str`: List of selected participant IDs
         """
-        return np.random.choice(
-            self.participant_ids, size=self.num_ids_to_select, replace=False
-        )
+
+        num_ids_to_select = self.get_num_ids_to_select(len(participant_ids))
+        return np.random.choice(participant_ids, size=num_ids_to_select, replace=False)
diff --git a/xain_fl/fl/coordinator/coordinator.py b/xain_fl/fl/coordinator/coordinator.py
deleted file mode 100644
index fd68ce649..000000000
--- a/xain_fl/fl/coordinator/coordinator.py
+++ /dev/null
@@ -1,279 +0,0 @@
-"""Class Coordinator orchestrates federated learning over a number of participants
-using a selection strategy (implemented through Controller sub-class) and an aggregation
-method (implemented through Aggregator sub-class).
-"""
-import concurrent.futures
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Tuple
-
-import tensorflow as tf
-from absl import flags
-
-from xain_fl.datasets import prep
-from xain_fl.fl.logging.logging import create_summary_writer, write_summaries
-from xain_fl.fl.participant import ModelProvider, Participant
-from xain_fl.logger import get_logger
-from xain_fl.types import History, Metrics, Partition, Theta
-
-from .aggregate import Aggregator, FederatedAveragingAgg
-
-FLAGS = flags.FLAGS
-
-logger = get_logger(__name__)
-
-
-class Coordinator:
-    """Central class of federated learning."""
-
-    # pylint: disable-msg=too-many-arguments
-    # pylint: disable=too-many-instance-attributes
-    def __init__(
-        self,
-        controller,
-        model_provider: ModelProvider,
-        participants: List[Participant],
-        C: float,
-        E: int,
-        xy_val: Partition,
-        aggregator: Optional[Aggregator] = None,
-    ) -> None:
-        """Initializes coordinator.
-
-        Args:
-            controller (Controller): Required selection strategy
-            model_provider (ModelProvider)
-            participants (List[Participant])
-            C (float): Fraction of participants selected in each round
-            E (int): Number of epochs in each round
-            xy_val (Partition): Validation data partition
-            aggregator (Optional[Aggregator] = None): Optional aggreation method, defaults
-                to FederatedAveragingAgg
-        """
-        self.controller = controller
-        self.model = model_provider.init_model()
-        self.participants = participants
-        self.C = C
-        self.E = E
-        self.xy_val = xy_val
-        self.aggregator = aggregator if aggregator else FederatedAveragingAgg()
-        self.epoch = 0  # Count training epochs
-
-    # Common initialization happens implicitly: By updating the participant weights to
-    # match the coordinator weights ahead of every training round we achieve common
-    # initialization.
-    def fit(
-        self, num_rounds: int
-    ) -> Tuple[History, List[List[History]], List[List[Dict]], List[List[Metrics]]]:
-        """Performs federated learning for a given number of rounds.
-
-        Args:
-            num_rounds (int): Number of rounds to run the federated learning
-
-        Returns:
-            Tuple[History, List[List[History]], List[List[Dict]], List[List[Metrics]]]
-        """
-        # Initialize history; history coordinator
-        hist_co: History = {"val_loss": [], "val_acc": []}
-        # Train rounds; training history of selected participants
-        hist_ps: List[List[History]] = []
-        # History of optimizer configs in each round
-        hist_opt_configs: List[List[Dict]] = []
-        # History of participant metrics in each round
-        hist_metrics: List[List[Metrics]] = []
-
-        # Defining log directory and file writer for tensorboard logging
-        val_log_dir: str = str(
-            Path(FLAGS.output_dir).joinpath("tensorboard/coordinator")
-        )
-        summary_writer = create_summary_writer(logdir=val_log_dir)
-
-        for r in range(num_rounds):
-            # Determine who participates in this round
-            indices = self.controller.select_ids()
-            msg = f"Round {r+1}/{num_rounds}: Participants {indices}"
-            logger.info(msg)
-
-            # Train
-            histories, opt_configs, train_metrics = self.fit_round(indices, self.E)
-            hist_ps.append(histories)
-            hist_opt_configs.append(opt_configs)
-            hist_metrics.append(train_metrics)
-
-            # Evaluate
-            val_loss, val_acc = self.evaluate(self.xy_val)
-            # Writing validation loss and accuracy into summary
-            write_summaries(
-                summary_writer=summary_writer,
-                val_acc=val_acc,
-                val_loss=val_loss,
-                train_round=r,
-            )
-            hist_co["val_loss"].append(val_loss)
-            hist_co["val_acc"].append(val_acc)
-
-        logger.info(
-            "TensorBoard coordinator validation logs saved in directory",
-            val_log_dir=val_log_dir,
-        )
-        logger.info(
-            'Detailed analysis: call "tensorboard --logdir {}" from the \
-            console and open "localhost:6006" in a browser'.format(
-                val_log_dir
-            )
-        )
-
-        return hist_co, hist_ps, hist_opt_configs, hist_metrics
-
-    def fit_round(
-        self, indices: List[str], E: int
-    ) -> Tuple[List[History], List[Dict], List[Metrics]]:
-        """Performs a single round of federated learning.
-
-        Args:
-            indices (List[int]): Selected indices for round.
-            E (int): Number of local epochs to train.
-
-        Returns:
-            Tuple[List[History], List[Dict], List[Metrics]]
-        """
-        theta = self.model.get_weights()
-        participants = [
-            participant
-            for participant in self.participants
-            if str(participant.cid) in indices
-        ]
-        # Collect training results from the participants of this round
-        theta_updates, histories, opt_configs, train_metrics = self.train_local_concurrently(
-            theta, participants, E
-        )
-        # Aggregate training results
-        theta_prime = self.aggregator.aggregate(theta_updates)
-        # Update own model parameters
-        self.model.set_weights(theta_prime)
-        self.epoch += E
-        return histories, opt_configs, train_metrics
-
-    def train_local_sequentially(
-        self, theta: Theta, participants: List[Participant], E: int
-    ) -> Tuple[List[Tuple[Theta, int]], List[History], List[Dict], List[Metrics]]:
-        """Train on each participant sequentially.
-
-        Args:
-            theta (Theta): Current global model parameters
-            participants (List[Participant]): Selected participants
-            E (int): Number of local training epochs
-
-        Returns:
-            Tuple[List[Tuple[Theta, int]], List[History], List[Dict], List[Metrics]]: Theta
-                primes, local training histories, optimizer configs, training metrics
-        """
-        theta_updates: List[Tuple[Theta, int]] = []
-        histories: List[History] = []
-        opt_configs: List[Dict] = []
-        train_metrics: List[Metrics] = []
-        for participant in participants:
-            # Train one round on this particular participant:
-            # - Push current model parameters to this participant
-            # - Train for a number of epochs
-            # - Pull updated model parameters from participant
-            theta_update, hist, opt_config = participant.train_round(
-                theta, epochs=E, epoch_base=self.epoch
-            )
-            metrics = participant.metrics()
-            theta_updates.append(theta_update)
-            histories.append(hist)
-            opt_configs.append(opt_config)
-            train_metrics.append(metrics)
-        return theta_updates, histories, opt_configs, train_metrics
-
-    def train_local_concurrently(
-        self, theta: Theta, participants: List[Participant], E: int
-    ) -> Tuple[List[Tuple[Theta, int]], List[History], List[Dict], List[Metrics]]:
-        """Train on each participant concurrently.
-
-        Args:
-            theta (Theta): Current global model parameters
-            participants (List[Participant]): Selected participants
-            E (int): Number of local training epochs
-
-        Returns:
-            Tuple[List[Tuple[Theta, int]], List[History], List[Dict], List[Metrics]]: Theta
-                primes, local training histories, optimizer configs, training metrics
-        """
-        theta_updates: List[Tuple[Theta, int]] = []
-        histories: List[History] = []
-        opt_configs: List[Dict] = []
-        train_metrics: List[Metrics] = []
-        # Wait for all futures to complete
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            future_results = [
-                executor.submit(_train_local, p, theta, E, self.epoch)
-                for p in participants
-            ]
-            concurrent.futures.wait(future_results)
-            for future in future_results:
-                theta_update, hist, opt_config, metrics = future.result()
-                theta_updates.append(theta_update)
-                histories.append(hist)
-                opt_configs.append(opt_config)
-                train_metrics.append(metrics)
-        return theta_updates, histories, opt_configs, train_metrics
-
-    def evaluate(self, xy_val: Partition) -> Tuple[float, float]:
-        """Evaluate the global model using the provided validation data.
-
-        Args:
-            xy_val (Partition)
-
-        Returns:
-            Tuple[float, float]: Loss and accuracy
-        """
-        ds_val = prep.init_ds_val(xy_val)
-        # Assume the validation `tf.data.Dataset` to yield exactly one batch containing
-        # all examples in the validation set
-        loss, accuracy = self.model.evaluate(ds_val, steps=1)
-        return float(loss), float(accuracy)
-
-    def num_participants(self) -> int:
-        """Returns number of participants
-
-        Returns:
-            int
-        """
-        return len(self.participants)
-
-
-def _train_local(
-    p: Participant, theta: Theta, epochs: int, epoch_base: int
-) -> Tuple[Tuple[Theta, int], History, Dict, Metrics]:
-    theta_update, history, opt_config = p.train_round(
-        theta, epochs=epochs, epoch_base=epoch_base
-    )
-    metrics = p.metrics()
-    return theta_update, history, opt_config, metrics
-
-
-# TODO: legacy code, needs refactor: https://xainag.atlassian.net/browse/XP-292
-def _abs_C(C: float, num_participants: int) -> int:
-    return int(min(num_participants, max(1, C * num_participants)))
-
-
-def _create_evalueate_fn(
-    orig_model: tf.keras.Model, xy_val: Partition
-) -> Callable[[Theta], Tuple[float, float]]:
-    ds_val = prep.init_ds_val(xy_val)
-    model = tf.keras.models.clone_model(orig_model)
-    # FIXME refactor model compilation
-    model.compile(
-        loss=tf.keras.losses.categorical_crossentropy,
-        optimizer=tf.keras.optimizers.Adam(),
-        metrics=["accuracy"],
-    )
-
-    def fn(theta: Theta) -> Tuple[float, float]:
-        model.set_weights(theta)
-        # Assume the validation `tf.data.Dataset` to yield exactly one batch containing
-        # all examples in the validation set
-        return model.evaluate(ds_val, steps=1)
-
-    return fn
diff --git a/xain_fl/fl/coordinator/coordinator_test.py b/xain_fl/fl/coordinator/coordinator_test.py
deleted file mode 100644
index 15dcd649f..000000000
--- a/xain_fl/fl/coordinator/coordinator_test.py
+++ /dev/null
@@ -1,51 +0,0 @@
-from .coordinator import _abs_C
-
-
-def test_abs_C_min():
-    # Prepare
-    num_participants = 100
-    C = 0.0
-    # Execute
-    actual = _abs_C(C, num_participants)
-    # Assert
-    assert actual == 1
-
-
-def test_abs_C_point_1():
-    # Prepare
-    num_participants = 100
-    C = 0.1
-    # Execute
-    actual = _abs_C(C, num_participants)
-    # Assert
-    assert actual == 10
-
-
-def test_abs_C_point_5():
-    # Prepare
-    num_participants = 100
-    C = 0.5
-    # Execute
-    actual = _abs_C(C, num_participants)
-    # Assert
-    assert actual == 50
-
-
-def test_abs_C_1():
-    # Prepare
-    num_participants = 100
-    C = 1.0
-    # Execute
-    actual = _abs_C(C, num_participants)
-    # Assert
-    assert actual == 100
-
-
-def test_abs_C_2():
-    # Prepare
-    num_participants = 100
-    C = 2.0
-    # Execute
-    actual = _abs_C(C, num_participants)
-    # Assert
-    assert actual == 100
diff --git a/xain_fl/fl/coordinator/evaluator.py b/xain_fl/fl/coordinator/evaluator.py
deleted file mode 100644
index 2b5a22238..000000000
--- a/xain_fl/fl/coordinator/evaluator.py
+++ /dev/null
@@ -1,21 +0,0 @@
-from typing import Tuple
-
-import tensorflow as tf
-
-from xain_fl.datasets import prep
-from xain_fl.types import Partition, Theta
-
-
-class Evaluator:
-    """Evaluates the model performance on a given data partition."""
-
-    def __init__(self, model: tf.keras.Model, xy_val: Partition) -> None:
-        self.model = model
-        self.ds_val = prep.init_ds_val(xy_val)
-
-    def evaluate(self, theta: Theta) -> Tuple[float, float]:
-        self.model.set_weights(theta)
-        # Assume the validation `tf.data.Dataset` to yield exactly one batch containing
-        # all examples in the validation set
-        loss, acc = self.model.evaluate(self.ds_val, steps=1)
-        return loss, acc
diff --git a/xain_fl/fl/logging/__init__.py b/xain_fl/fl/logging/__init__.py
deleted file mode 100644
index d9cd29bb9..000000000
--- a/xain_fl/fl/logging/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Provides capability to log using TensorBoard"""
diff --git a/xain_fl/fl/logging/logging.py b/xain_fl/fl/logging/logging.py
deleted file mode 100644
index 714d48d15..000000000
--- a/xain_fl/fl/logging/logging.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""Functions for logging TensorBoard summaries"""
-
-import tensorflow as tf
-from absl import flags
-from tensorflow._api.v1.compat.v1 import Summary
-from tensorflow._api.v1.compat.v1.summary import FileWriter
-
-FLAGS = flags.FLAGS
-
-
-def create_summary_writer(logdir: str) -> FileWriter:
-    """Creating a summary FileWriter.
-
-    Creates a FileWriter that create an event file in a given directory and add summaries and
-    events to it. The file contents are updated asynchronously.
-
-    Args:
-        logdir (str): Directory in which the tensorboard evnt log is written.
-
-    Returns:
-        ~tf.summary.FileWriter: FileWriter object writing Summaries to event files.
-    """
-
-    summary_writer = FileWriter(logdir=logdir, graph=tf.compat.v1.get_default_graph())
-    return summary_writer
-
-
-def write_summaries(
-    summary_writer: FileWriter, val_acc: float, val_loss: float, train_round: int
-) -> None:
-    """Adding summaries to an event file.
-
-    Adds validation loss and accuracy as scalar values, as well as the global step value to the
-    event file.
-
-    Args:
-        summary_writer (~tf.summary.FileWriter): FileWriter object writing Summaries to event files.
-        val_acc (float): Validation accuracy that should be logged to event file.
-        val_loss (float): Validation loss that should be logged to event file.
-        train_round (int): Train round, that should be logged as dependency for acc and loss.
-    """
-
-    summary_writer.add_summary(
-        summary=Summary(
-            value=[
-                Summary.Value(
-                    tag=f"coordinator_{FLAGS.task_name}/val_acc", simple_value=val_acc
-                )
-            ]
-        ),
-        global_step=train_round,
-    )
-
-    summary_writer.add_summary(
-        summary=Summary(
-            value=[
-                Summary.Value(
-                    tag=f"coordinator_{FLAGS.task_name}/val_loss", simple_value=val_loss
-                )
-            ]
-        ),
-        global_step=train_round,
-    )
-    # flushing each training round to observe live training in TensorBoard dashboard
-    summary_writer.flush()
diff --git a/xain_fl/fl/participant/__init__.py b/xain_fl/fl/participant/__init__.py
deleted file mode 100644
index 1a4574329..000000000
--- a/xain_fl/fl/participant/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-"""Contains partiticpant logic which runs the local training in federated
-learning. It consists of the following modules:
-
-    - model_provider
-    - participant
-"""
-from .model_provider import ModelProvider
-from .participant import Participant
diff --git a/xain_fl/fl/participant/model_provider.py b/xain_fl/fl/participant/model_provider.py
deleted file mode 100644
index 1449143c5..000000000
--- a/xain_fl/fl/participant/model_provider.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Provides the model provider abstraction."""
-from typing import Callable
-
-import tensorflow as tf
-
-
-class ModelProvider:
-    """Encapsulates a compiled Keras model and an optional
-    associated learning rate function.
-    """
-
-    def __init__(self, model_fn: Callable[[], tf.keras.Model], lr_fn_fn: Callable):
-        self.init_model = model_fn
-        self.init_lr_fn = lr_fn_fn
diff --git a/xain_fl/fl/participant/participant.py b/xain_fl/fl/participant/participant.py
deleted file mode 100644
index e5ec25d02..000000000
--- a/xain_fl/fl/participant/participant.py
+++ /dev/null
@@ -1,175 +0,0 @@
-"""Class Participant handles local training in federated learning using its own
-data partition to refine the global model.
-"""
-from typing import Dict, List, Tuple
-
-import numpy as np
-import tensorflow as tf
-
-from xain_fl.datasets import prep
-from xain_fl.logger import get_logger
-from xain_fl.types import History, Metrics, Partition, Theta, VolumeByClass
-
-from .model_provider import ModelProvider
-
-logger = get_logger(__name__)
-
-
-class Participant:
-    # pylint: disable-msg=too-many-arguments
-    # pylint: disable=too-many-instance-attributes
-    def __init__(
-        self,
-        cid: int,
-        model_provider: ModelProvider,
-        xy_train: Partition,
-        xy_val: Partition,
-        num_classes: int,
-        batch_size: int,
-        use_lr_fn: bool = True,
-    ) -> None:
-        """Initializes the participant
-
-        Args:
-            cid (int): Participant identifier
-            model_provider (ModelProvider)
-            xy_train (Partition): Training data
-            xy_val (Partition): Validation data
-            num_classes (int): Number of classes (classification)
-            batch_size (int)
-            use_lr_fn (bool = True): Optional boolean to enable learning rate function,
-                defaults to True
-        """
-        assert xy_train[0].shape[0] == xy_train[1].shape[0]
-        assert xy_val[0].shape[0] == xy_val[1].shape[0]
-        self.cid = cid
-        self.model_provider = model_provider
-        self.num_classes: int = num_classes
-        self.batch_size: int = batch_size
-        self.use_lr_fn: bool = use_lr_fn
-        self.num_examples = xy_train[0].shape[0]
-        # Training set
-        self.xy_train = xy_train
-        self.steps_train: int = int(xy_train[0].shape[0] / batch_size)
-        # Validation set
-        self.xy_val = xy_val
-        self.steps_val: int = 1
-
-    def train_round(
-        self, theta: Theta, epochs: int, epoch_base: int
-    ) -> Tuple[Tuple[Theta, int], History, Dict]:
-        """Performs the responsibilities of a single participant in a single round
-            of federated learning.
-
-        Args:
-            theta (Theta): Current global model
-            epochs (int): Number of local training epochs
-            epoch_base (int): Number of globally performed epochs
-
-        Returns:
-            Tuple[Tuple[Theta, int], History, Dict]: Theta prime, local training history,
-                and optimizer configs
-        """
-        logger.info(
-            "Participant: train_round START",
-            participant_cid=self.cid,
-            epoch_base=epoch_base,
-        )
-        model = self.model_provider.init_model(epoch_base=epoch_base)  # type:ignore
-        model.set_weights(theta)
-
-        callbacks: List = []
-        if self.use_lr_fn:
-            lr_fn = self.model_provider.init_lr_fn(epoch_base=epoch_base)  # type:ignore
-            callback_lr = tf.keras.callbacks.LearningRateScheduler(lr_fn)
-            callbacks = [callback_lr]
-
-        hist: History = self._fit(model, epochs, callbacks)
-        theta_prime = model.get_weights()
-        opt_config = model.optimizer.get_config()
-        opt_config = _convert_numpy_types(opt_config)
-        logger.info("Participant: train_round FINISH", participant_cid=self.cid)
-        return (theta_prime, self.num_examples), hist, opt_config
-
-    def _fit(self, model: tf.keras.Model, epochs: int, callbacks: List) -> History:
-        ds_train = prep.init_ds_train(self.xy_train, self.num_classes, self.batch_size)
-        ds_val = prep.init_ds_val(self.xy_val, self.num_classes)
-
-        callback_logging = LoggingCallback(str(self.cid), logger.info)
-        callbacks.append(callback_logging)
-
-        hist = model.fit(
-            ds_train,
-            epochs=epochs,
-            validation_data=ds_val,
-            callbacks=callbacks,
-            shuffle=False,  # Shuffling is handled via tf.data.Dataset
-            steps_per_epoch=self.steps_train,
-            validation_steps=self.steps_val,
-            verbose=0,
-        )
-        return _cast_to_float(hist.history)
-
-    def evaluate(self, theta: Theta, xy_test: Partition) -> Tuple[float, float]:
-        """Evaluate the local model using the provided validation data.
-
-        Args:
-            theta (Theta)
-            xy_test (Partition)
-
-        Returns:
-            Tuple[float, float]: Loss and accuracy
-        """
-        model = self.model_provider.init_model()
-        model.set_weights(theta)
-        ds_val = prep.init_ds_val(xy_test)
-        # Assume the validation `tf.data.Dataset` to yield exactly one batch containing
-        # all examples in the validation set
-        loss, accuracy = model.evaluate(ds_val, steps=1, verbose=0)
-        return loss, accuracy
-
-    def metrics(self) -> Metrics:
-        """Provides training metrics.
-
-        Returns:
-            (int, VolumeByClass)
-        """
-        vol_by_class = _xy_train_volume_by_class(self.num_classes, self.xy_train)
-        return (self.cid, vol_by_class)
-
-
-def _xy_train_volume_by_class(num_classes: int, xy_train) -> VolumeByClass:
-    counts = [0] * num_classes
-    _, y = xy_train
-    classes, counts_actual = np.unique(y, return_counts=True)
-
-    for i_ca, c in enumerate(classes):
-        # Cast explicitly to int so its later JSON serializable
-        # as other we will get a list of np objects of type int64
-        counts[c] = int(counts_actual[i_ca])
-
-    return counts
-
-
-def _cast_to_float(hist) -> History:
-    for key in hist:
-        for index, number in enumerate(hist[key]):
-            hist[key][index] = float(number)
-    return hist
-
-
-def _convert_numpy_types(opt_config: Dict) -> Dict:
-    for key in opt_config:
-        if isinstance(opt_config[key], np.float32):
-            opt_config[key] = opt_config[key].item()
-    return opt_config
-
-
-class LoggingCallback(tf.keras.callbacks.Callback):
-    def __init__(self, cid: str, print_fn):
-        tf.keras.callbacks.Callback.__init__(self)
-        self.cid = cid
-        self.print_fn = print_fn
-
-    def on_epoch_end(self, epoch, logs=None):
-        self.print_fn(f"CID {self.cid} epoch {epoch}")
diff --git a/xain_fl/fl/participant/participant_test.py b/xain_fl/fl/participant/participant_test.py
deleted file mode 100644
index 8c9bd49b4..000000000
--- a/xain_fl/fl/participant/participant_test.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import numpy as np
-import pytest
-
-# from benchmarks.benchmark.net import model_fns  # FIXME refactor
-from xain_fl.datasets import load_splits
-
-# from .model_provider import ModelProvider  # FIXME refactor
-from .participant import Participant, _xy_train_volume_by_class
-
-
-def test_Participant_x_y_shape_valid():
-    # Prepare
-    m = None
-    x = np.zeros((5, 32, 32, 3), dtype=np.uint8)
-    y = np.zeros((5), dtype=np.uint8)
-    # Execute
-    _ = Participant(0, m, (x, y), (x, y), num_classes=10, batch_size=32)
-    # pass if initialization does not raise an exception
-
-
-def test_Participant_x_y_shape_invalid():
-    # Prepare
-    m = None
-    x = np.zeros((3, 32, 32, 3), dtype=np.uint8)
-    y = np.zeros((4), dtype=np.uint8)
-    # Execute & assert
-    try:
-        _ = Participant(0, m, (x, y), (x, y), num_classes=10, batch_size=32)
-        pytest.fail("No AssertionError raised")
-    except AssertionError:
-        pass
-
-
-# FIXME refactor to remove dependency on benchmark code
-# def test_Participant_num_examples():
-#     # Prepare
-#     num_examples_expected = 19
-#     num_classes = 10
-#     model_provider = ModelProvider(model_fns["blog_cnn"], lr_fn_fn=None)
-#     x = np.random.randint(
-#         0, high=256, size=(num_examples_expected, 28, 28, 1), dtype=np.uint8
-#     )
-#     y = np.random.randint(
-#         0, high=num_classes, size=(num_examples_expected), dtype=np.uint8
-#     )
-#     participant = Participant(
-#         0,
-#         model_provider,
-#         (x, y),
-#         (x, y),
-#         num_classes=num_classes,
-#         batch_size=16,
-#         use_lr_fn=False,
-#     )
-#     weights = model_provider.init_model().get_weights()
-#
-#     # Execute
-#     (_, num_examples_actual), _, _ = participant.train_round(weights, 2, 0)
-#
-#     # Assert
-#     assert num_examples_actual == num_examples_expected
-
-
-def test_Participant_get_xy_train_volume_by_class():
-    # Prepare
-    cid_expected = 19
-    num_classes = 5
-    m = None
-    x = np.zeros((4, 32, 32, 3), dtype=np.uint8)
-    y = np.array([0, 1, 2, 2], dtype=np.uint8)
-
-    y_volume_by_class_expected = [1, 1, 2, 0, 0]
-
-    # Execute
-    p = Participant(
-        cid_expected, m, (x, y), (x, y), num_classes=num_classes, batch_size=32
-    )
-
-    # Assert
-    (cid_actual, y_volume_by_class_actual) = p.metrics()
-
-    assert cid_actual == cid_expected
-    assert y_volume_by_class_actual == y_volume_by_class_expected
-
-
-@pytest.mark.parametrize(
-    "num_classes_total, num_classes_in_partition", [(4, 1), (7, 5), (10, 10)]
-)
-def test_xy_train_volume_by_class(num_classes_total, num_classes_in_partition):
-    # Prepare
-    y_train = np.arange(num_classes_in_partition, dtype=np.int8)
-    x_train = np.ones((y_train.size))  # not relevant; only needed to avoid type errors
-    xy_train = (x_train, y_train)
-
-    # Execute
-    result = _xy_train_volume_by_class(num_classes=num_classes_total, xy_train=xy_train)
-
-    # Assert
-    assert len(result) == num_classes_total
-    if num_classes_total == num_classes_in_partition:
-        # As each class is equal times present the set should contain only one element
-        assert set(result) == {1}
-    else:
-        # As each class is equal or zero times present the set should contain 1 and 0
-        assert set(result) == {0, 1}
-
-
-@pytest.mark.slow
-@pytest.mark.integration
-def test_xy_train_volume_by_class_with_federated_dataset():
-    # Prepare
-    dataset_name = "fashion-mnist-100p-b1_045"
-    xy_partitions, _, _ = load_splits(dataset_name)
-    num_examples_expected = sum([x.shape[0] for x, _ in xy_partitions])
-
-    # We need to find out which classes are present in our dataset
-    # (actually we know it but making it a bit more adaptable in case we parameterize it)
-    all_classes = set()
-
-    for _, y_train in xy_partitions:
-        classes = np.unique(y_train)
-        for c in classes:
-            all_classes.add(c)
-
-    num_classes_total = len(all_classes)
-
-    results = []
-
-    # Execute
-    for xy_train in xy_partitions:
-        _, y_train = xy_train
-        r = _xy_train_volume_by_class(num_classes=num_classes_total, xy_train=xy_train)
-        results.append(r)
-
-    # Assert
-    num_examples_actual = 0
-
-    for r in results:
-        num_examples_actual += sum(r)
-
-    assert num_examples_expected == num_examples_actual
diff --git a/xain_fl/fl/types/__init__.py b/xain_fl/fl/types/__init__.py
new file mode 100644
index 000000000..7525d0b1c
--- /dev/null
+++ b/xain_fl/fl/types/__init__.py
@@ -0,0 +1,13 @@
+"""XAIN FL types"""
+
+from typing import Dict, List, Tuple
+
+from numpy import ndarray
+
+Theta = List[ndarray]
+# TODO: (XP-241) Remove once participant was removed
+History = Dict[str, List[float]]
+
+# TODO: (XP-241) Remove once participant was removed
+VolumeByClass = List[int]
+Metrics = Tuple[int, VolumeByClass]
diff --git a/benchmarks/__init__.py b/xain_fl/helloproto/__init__.py
similarity index 100%
rename from benchmarks/__init__.py
rename to xain_fl/helloproto/__init__.py
diff --git a/xain_fl/cproto/numproto_client.py b/xain_fl/helloproto/numproto_client.py
similarity index 57%
rename from xain_fl/cproto/numproto_client.py
rename to xain_fl/helloproto/numproto_client.py
index 044ff7bc1..bd8c444e1 100644
--- a/xain_fl/cproto/numproto_client.py
+++ b/xain_fl/helloproto/numproto_client.py
@@ -1,14 +1,21 @@
+"""XAIN FL numproto client"""
+
 import grpc
 import numpy as np
-from numproto import ndarray_to_proto, proto_to_ndarray
+from xain_proto.fl import hellonumproto_pb2, hellonumproto_pb2_grpc
+from xain_proto.numproto import ndarray_to_proto, proto_to_ndarray
 
-from xain_fl.cproto import hellonumproto_pb2, hellonumproto_pb2_grpc
-from xain_fl.logger import get_logger
+from xain_fl.logger import StructLogger, get_logger
 
-logger = get_logger(__name__)
+logger: StructLogger = get_logger(__name__)
 
 
 def run():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
     with grpc.insecure_channel("localhost:50051") as channel:
         stub = hellonumproto_pb2_grpc.NumProtoServerStub(channel)
 
diff --git a/xain_fl/helloproto/numproto_server.py b/xain_fl/helloproto/numproto_server.py
new file mode 100644
index 000000000..5e44f7880
--- /dev/null
+++ b/xain_fl/helloproto/numproto_server.py
@@ -0,0 +1,63 @@
+"""XAIN FL numproto server"""
+
+from concurrent import futures
+import time
+
+import grpc
+from xain_proto.fl import hellonumproto_pb2, hellonumproto_pb2_grpc
+from xain_proto.numproto import ndarray_to_proto, proto_to_ndarray
+
+from xain_fl.coordinator import _ONE_DAY_IN_SECONDS
+from xain_fl.logger import StructLogger, get_logger
+
+logger: StructLogger = get_logger(__name__)
+
+
+class NumProtoServer(  # pylint: disable=too-few-public-methods
+    hellonumproto_pb2_grpc.NumProtoServerServicer
+):
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    def SayHelloNumProto(self, request, context):
+        """[summary]
+
+        .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+
+        Args:
+            request ([type]): [description]
+            context ([type]): [description]
+
+        Returns:
+            [type]: [description]
+        """
+
+        nda = proto_to_ndarray(request.arr)
+        logger.info("NumProto server received", nda=nda)
+
+        nda *= 2
+        logger.info("NumProto server sent", nda=nda)
+        return hellonumproto_pb2.NumProtoReply(arr=ndarray_to_proto(nda))
+
+
+def serve():
+    """[summary]
+
+    .. todo:: Advance docstrings (https://xainag.atlassian.net/browse/XP-425)
+    """
+
+    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
+    hellonumproto_pb2_grpc.add_NumProtoServerServicer_to_server(NumProtoServer(), server)
+    server.add_insecure_port("[::]:50051")
+    server.start()
+    try:
+        while True:
+            time.sleep(_ONE_DAY_IN_SECONDS)
+    except KeyboardInterrupt:
+        server.stop(0)
+
+
+if __name__ == "__main__":
+    serve()
diff --git a/xain_fl/helpers/__init__.py b/xain_fl/helpers/__init__.py
deleted file mode 100644
index 830d198d4..000000000
--- a/xain_fl/helpers/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Provides small helper methods which more than one package needs
-"""
diff --git a/xain_fl/helpers/project.py b/xain_fl/helpers/project.py
deleted file mode 100644
index 00d214656..000000000
--- a/xain_fl/helpers/project.py
+++ /dev/null
@@ -1,14 +0,0 @@
-import os
-from pathlib import Path
-
-module_dir = os.path.dirname(__file__)  # directory in which this module resides
-root_dir = os.path.abspath(os.path.join(module_dir, "../../"))  # project root dir
-
-
-def root() -> Path:
-    """Get project root directory
-
-    Returns:
-        Path: path object for project root directory
-    """
-    return Path(root_dir)
diff --git a/xain_fl/helpers/sha1.py b/xain_fl/helpers/sha1.py
deleted file mode 100644
index 105d5cb96..000000000
--- a/xain_fl/helpers/sha1.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import hashlib
-
-
-def checksum(fpath: str) -> str:
-    """Return checksum of file a fpath
-
-    Args:
-        fpath (str): absolute path to file
-
-    Returns:
-        str: sha1.hexdigest() of file
-    """
-    sha1 = hashlib.sha1()
-
-    with open(fpath, "rb") as f:
-        while True:
-            data = f.read()
-            if not data:
-                break
-            sha1.update(data)
-
-    return sha1.hexdigest()
diff --git a/xain_fl/logger.py b/xain_fl/logger.py
index 883a3bed6..2c475d6a7 100644
--- a/xain_fl/logger.py
+++ b/xain_fl/logger.py
@@ -1,11 +1,11 @@
+"""XAIN FL structured logger"""
+
 import logging
 from typing import Any
 
 import structlog
 
-StructLogger = (
-    structlog._config.BoundLoggerLazyProxy  # pylint: disable=protected-access
-)
+StructLogger = structlog._config.BoundLoggerLazyProxy  # pylint: disable=protected-access
 
 
 def get_logger(
@@ -20,15 +20,11 @@ def get_logger(
     """
     AIMETRICS = 25  # pylint: disable=invalid-name
     structlog.stdlib.AIMETRICS = AIMETRICS
-    structlog.stdlib._NAME_TO_LEVEL[  # pylint: disable=protected-access
-        "aimetrics"
-    ] = AIMETRICS
-    structlog.stdlib._LEVEL_TO_NAME[  # pylint: disable=protected-access
-        AIMETRICS
-    ] = "aimetrics"
+    structlog.stdlib._NAME_TO_LEVEL["aimetrics"] = AIMETRICS  # pylint: disable=protected-access
+    structlog.stdlib._LEVEL_TO_NAME[AIMETRICS] = "aimetrics"  # pylint: disable=protected-access
     logging.addLevelName(AIMETRICS, "aimetrics")
 
-    def aimetrics(self, msg: str, *args, **kw) -> Any:  # type: ignore
+    def aimetrics(self, msg: str, *args: Any, **kw: Any) -> Any:
         return self.log(AIMETRICS, msg, *args, **kw)
 
     structlog.stdlib._FixedFindCallerLogger.aimetrics = (  # pylint: disable=protected-access
diff --git a/xain_fl/sdk/__init__.py b/xain_fl/sdk/__init__.py
deleted file mode 100644
index 857509f71..000000000
--- a/xain_fl/sdk/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Provides xain-fl package SDK"""
diff --git a/xain_fl/sdk/coordinator.py b/xain_fl/sdk/coordinator.py
deleted file mode 100644
index e8c8372fe..000000000
--- a/xain_fl/sdk/coordinator.py
+++ /dev/null
@@ -1,10 +0,0 @@
-"""Provides coordinator API"""
-
-
-def start():
-    """Starts a coordinator
-
-    TODO: provide through console scripts
-    TODO: IMPLEMENT
-    """
-    raise NotImplementedError
diff --git a/xain_fl/sdk/coordinator_test.py b/xain_fl/sdk/coordinator_test.py
deleted file mode 100644
index b510a87e0..000000000
--- a/xain_fl/sdk/coordinator_test.py
+++ /dev/null
@@ -1,8 +0,0 @@
-import pytest
-
-from . import coordinator
-
-
-@pytest.mark.xfail
-def test_start():
-    coordinator.start()
diff --git a/xain_fl/sdk/participant.py b/xain_fl/sdk/participant.py
deleted file mode 100644
index 65bc611b3..000000000
--- a/xain_fl/sdk/participant.py
+++ /dev/null
@@ -1,14 +0,0 @@
-"""Provides participant API"""
-from .use_case import UseCase
-
-
-def start(coordinator_url: str, use_case: UseCase):
-    """Starts a participant which will connect to coordinator_url and
-    work on use_case
-
-    Args:
-        coordinator_url (str): URL of the coordinator to connect to
-        use_case (UseCase): Instance of UseCase class
-    """
-
-    raise NotImplementedError
diff --git a/xain_fl/sdk/participant_test.py b/xain_fl/sdk/participant_test.py
deleted file mode 100644
index 97dd746c9..000000000
--- a/xain_fl/sdk/participant_test.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import pytest
-
-from . import participant
-from .use_case import UseCase
-
-
-@pytest.mark.xfail
-def test_start():
-    class MyUseCase(UseCase):
-        def __init__(self, model, *args, **kwargs):
-            super().__init__(model, *args, **kwargs)
-            self.model = model
-
-        def set_weights(self, weights):
-            pass
-
-        def get_weights(self):
-            pass
-
-        def train(self):
-            pass
-
-    my_use_case = MyUseCase(model={})
-
-    participant.start(coordinator_url="http://localhost:8601", use_case=my_use_case)
diff --git a/xain_fl/sdk/use_case.py b/xain_fl/sdk/use_case.py
deleted file mode 100644
index c7e47a8a6..000000000
--- a/xain_fl/sdk/use_case.py
+++ /dev/null
@@ -1,41 +0,0 @@
-"""Provides abstract base class use_case which provides an interface to the
-participant runner"""
-from abc import ABC, abstractmethod
-from typing import List
-
-from numpy import ndarray
-
-
-class UseCase(ABC):
-    def __init__(self, model):
-        self.model = model
-
-    @abstractmethod
-    def set_weights(self, weights: List[ndarray]) -> None:
-        """Will be called by the runner to set weights of model. The implementation
-        should persist the weights so that they are used in a subsequent call to
-        the train method.
-
-        Args:
-            weights (List[ndarray]): Model parameters
-        """
-        raise NotImplementedError()
-
-    @abstractmethod
-    def get_weights(self) -> List[ndarray]:
-        """"Will be called by the runner after a train invocation to retrieve the
-        weights of the model. The implementation should return the model weights.
-
-        Returns:
-            List[ndarray]: Model parameters
-        """
-        raise NotImplementedError()
-
-    @abstractmethod
-    def train(self):
-        """Will be called by the runner to start the training of the model. The
-        implementation should run the training when called and before returning
-        from the method persist the weights so that in a subsequent get_weights
-        call the correct weights are returned
-        """
-        raise NotImplementedError()
diff --git a/xain_fl/sdk/use_case_test.py b/xain_fl/sdk/use_case_test.py
deleted file mode 100644
index 630ecafc9..000000000
--- a/xain_fl/sdk/use_case_test.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from .use_case import UseCase
-
-
-def test_UseCase():
-    class MyUseCase(UseCase):
-        def __init__(self):
-            super().__init__(self)
-
-        def set_weights(self, weights):
-            pass
-
-        def get_weights(self):
-            pass
-
-        def train(self):
-            pass
-
-    MyUseCase()
diff --git a/xain_fl/serve.py b/xain_fl/serve.py
index 0dd9a0d93..dec58c941 100644
--- a/xain_fl/serve.py
+++ b/xain_fl/serve.py
@@ -1,20 +1,23 @@
+"""XAIN FL serving"""
+
+from concurrent import futures
 import threading
 import time
-from concurrent import futures
 
 import grpc
+from xain_proto.fl import coordinator_pb2_grpc
 
 from xain_fl.coordinator import _ONE_DAY_IN_SECONDS
 from xain_fl.coordinator.coordinator import Coordinator
 from xain_fl.coordinator.coordinator_grpc import CoordinatorGrpc
 from xain_fl.coordinator.heartbeat import monitor_heartbeats
-from xain_fl.cproto import coordinator_pb2_grpc
-from xain_fl.logger import get_logger
+from xain_fl.coordinator.store import Store
+from xain_fl.logger import StructLogger, get_logger
 
-logger = get_logger(__name__)
+logger: StructLogger = get_logger(__name__)
 
 
-def serve(coordinator: Coordinator, host: str = "[::]", port: int = 50051) -> None:
+def serve(coordinator: Coordinator, store: Store, host: str = "[::]", port: int = 50051) -> None:
     """Main method to start the gRPC service.
 
     This methods just creates the :class:`xain_fl.coordinator.coordinator.Coordinator`,
@@ -27,7 +30,7 @@ def serve(coordinator: Coordinator, host: str = "[::]", port: int = 50051) -> No
 
     server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
     coordinator_pb2_grpc.add_CoordinatorServicer_to_server(
-        CoordinatorGrpc(coordinator), server
+        CoordinatorGrpc(coordinator, store), server
     )
     server.add_insecure_port(f"{host}:{port}")
     server.start()
diff --git a/xain_fl/tools/exceptions.py b/xain_fl/tools/exceptions.py
index 0906b0a8f..46aa7340e 100644
--- a/xain_fl/tools/exceptions.py
+++ b/xain_fl/tools/exceptions.py
@@ -1,3 +1,6 @@
+"""XAIN FL exceptions"""
+
+
 class DuplicatedUpdateError(Exception):
     """Exception raised when the same participant tries to submit multiple
     updates to the :class:`xain_fl.coordinator.coordinator.Coordinator` in the same :class:`~.Round`
@@ -18,5 +21,5 @@ class InvalidRequestError(Exception):
 
     This can happen if the participant sends a request that is not allowed in a
     give Coordinator state. For instance the Coordinator will only accept
-    StartTraining requests during a ROUND.
+    StartTrainingRound requests during a ROUND.
     """
diff --git a/xain_fl/types/__init__.py b/xain_fl/types/__init__.py
deleted file mode 100644
index 80b4ffe6f..000000000
--- a/xain_fl/types/__init__.py
+++ /dev/null
@@ -1,23 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple
-
-from numpy import ndarray
-
-# Returned from keras
-KerasDataset = Tuple[Tuple[ndarray, ndarray], Tuple[ndarray, ndarray]]
-
-Partition = Tuple[ndarray, ndarray]
-FederatedDataset = Tuple[List[Partition], Partition, Partition]
-
-FnameNDArrayTuple = Tuple[str, ndarray]
-
-Transition = Tuple[ndarray, Any, float, ndarray, bool]
-
-Theta = List[ndarray]
-History = Dict[str, List[float]]
-
-VolumeByClass = List[int]
-Metrics = Tuple[int, VolumeByClass]
-
-PlotValues = Tuple[str, List[float], Optional[List[int]]]
-XticksLocations = List[int]
-XticksLabels = List[str]