Behavior Mimics Distribution: Combining Individual and Group Behaviors for Federated Learning

Understand

Federated Learning (FL) has become an active and promising distributed machine learning paradigm.

• As a result of statistical heterogeneity, recent studies clearly show that the performance of popular FL methods (e.g., FedAvg) deteriorates dramatically due to the client drift caused by local updates.
• This paper proposes a novel Federated Learning algorithm (called IGFL), which leverages both Individual and Group behaviors to mimic distribution, thereby improving the ability to deal with heterogeneity.
• Unlike existing FL methods, our IGFL can be applied to both client and server optimization.