Balancing generalization and personalization by sharing layers in clustered federated learning

Clustered Federated Learning (CFL) is a modern approach that addresses the heterogeneous settings in Federated Learning. However, conventional models often fall into overfitting to the data within the cluster due to insufficient attention to communication between clients in different clusters. To tackle the issue of insufficient knowledge sharing in CFL, we present a novel approach to CFL that facilitates inter-cluster communication. Our proposed method promotes effective knowledge sharing among clusters while also improving the efficiency of clustering by layer separation. Experimental results show that our proposed method significantly enhances the model’s performance.