[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
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Updated
Jun 7, 2023 - Python
[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
Multigraph fusion and classification network using graph neural network
Library to simulate a distributed learning scenario, with clusters of users that train models minimizing a local cost function, and a server that wants to minimize a global cost function. The aim of the project is to study the tradeoff between local and global accuracy.
Papers related to Federated Learning in all top venues
Federated Learning (FL) is a collaborative machine learning approach that enables decentralized data processing. Instead of collecting and storing data in a central server, FL trains machine learning models directly on devices or servers where the data resides, enhancing privacy and security.
[ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"
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