Go to ICLR 2025 Conference homepagePersonalized Federated Learning With Similarity Information Supervisor
A crucial issue in federated learning is the heterogeneity of data between clients, which can lead to model weight divergence, eventually deteriorating the model performance. Personalized federated learning (pFL) has been proven to be an effective approach to addressing data heterogeneity in federated learning. However, existing pFL studies seldom verify whether the broadcast global model is beneficial for the local model performance. To address this, we propose a novel pFL method, called federated learning with similarity information supervision (FedSimSup). Specifically, FedSimSup incorporates a local supervisor to assist the model training and a personalized model for global information aggregation. The role of the supervisor is to refine the personalized model when it is not beneficial for the local model performance, ensuring the effective global information aggregation while aligning with the local heterogeneous data. Additionally, the similarity relationships between the clients are measured using label distribution differences of the local raw data to weight the personalized models, promoting information usage among similar clients. Experimental results demonstrate three advantages of FedSimSup: (1) It shows better performance over heterogeneous data compared with seven state-of-the-art federated learning methods; (2) It can allow for different model architectures across different clients; (3) It offers a certain degree of interpretability.