Merging Subgroup Information to Supplement Personal Information for Personalized Federated Learning Through Similar Client Grouping

Personalized federated learning represents a pivotal strategy for addressing the challenges posed by statistical heterogeneity in federated learning. Clients optimize their models by leveraging information from other clients through a global model. However, data heterogeneity constrains the generalization capacity of the global model, thereby degrading the feature representation capability of local client models, especially in clients with limited data. In response to this challenge, we propose the Federal Merging Subgroup Information (FedMSI) method to augment personalized information in personalized federated learning. On the server side, FedMSI employs model clustering to identify subgroups of clients with similar personalized data distributions. It then aggregates cluster center models within each subgroup and transmits them to clients for use in the subsequent round of assisted training. On the client side, FedMSI introduces a local optimization objective that incorporates the cluster center model, enabling the extraction of informative knowledge to enhance local training. Experiments demonstrate the effectiveness of FedMSI across different datasets, data heterogeneity levels, and data sizes. Ablation experiments further confirm the effectiveness of the design of the local optimization objective. Compared to state-of-the-art methods, FedMSI achieves a 13.16% improvement in scalability performance accuracy.