fix typos, rename

docs/fl_introduction.rst

@@ -9,29 +9,19 @@ This enables the creation of common robust models without sharing sensitive loca
 
 How does Federated Learning Work?
 =================================
-The federated learning (FL) aggregrator orchestrates the collaboration of multiple clients by first sending an initial model to the FL clients.
-The clients perform training on their local datasets, then send the model updates back to the FL aggregrator for aggregation to form a global model.
+The federated learning (FL) server orchestrates the collaboration of multiple clients by first sending an initial model to the FL clients.
+The clients perform training on their local datasets, then send the model updates back to the FL server for aggregation to form a global model.
 This process forms a single round of federated learning and after a number of rounds, a robust global model can be developed.
 
-.. note::
-
-    In the diagrams below, the FL server has the role of the FL aggregrator. In the case of client-controlled workflows such as swarm learning,
-    FL clients can serve as FL aggregrators instead.
-
 .. image:: resources/fl_diagram.png
     :height: 500px
     :align: center
 
 FL Terms and Definitions
 ========================
 
-- FL server: manages job lifecycle
-- FL aggregrator: orchestrates workflow, assigns tasks to clients, performs aggregation
-- FL client: executes tasks, performs local computation/learning with local dataset, submits result back to FL aggregrator
-
-.. image:: resources/controller_worker_flow.png
-    :height: 350px
-
+- FL server: manages job lifecycle, orchestrates workflow, assigns tasks to clients, performs aggregation
+- FL client: executes tasks, performs local computation/learning with local dataset, submits result back to FL server
 - FL algorithms: FedAvg, FedOpt, FedProx etc. implemented as workflows
 - Types of FL
 

examples/hello-world/step-by-step/README.md

@@ -9,7 +9,7 @@ These step-by-step example series are aimed to help users quickly get started an
 For consistency, each example in the series uses the same dataset- CIFAR10 for image data and the HIGGS dataset for tabular data.
 The examples will build upon previous ones to showcase different features, workflows, or APIs, allowing users to gain a comprehensive understanding of FLARE functionalities. See the README in each directory for more details about each series.
 
-## Key Ideas
+## Common Questions
 
 Here are some common questions we aim to cover in these examples series when formulating a federated learning problem: