Rethinking Personalized Federated Learning from Knowledge Perspective

Dezhong Yao, Ziquan Zhu, Tongtong Liu, Zhiqiang Xu, Hai Jin

Abstract: Personalized federated learning (PFL) is a variant of federation learning, which improves the model performance of each participant through collaborative training while providing a customized local model that meets their unique needs. Balancing global (general) knowledge and local (personalized) knowledge is a core challenge in PFL. Existing PFL methods neglect knowledge forgetting during aggregation and updating, impacting the fusion of both types of knowledge. We empirically confirm the existence of knowledge forgetting, which leads to performance degradation during FL. This observation motivates us to rethink PFL from a knowledge perspective. Therefore, we propose Personalized Federated Learning Framework with Adaptive Model Fusion (pFedAMF). We consider the global model as global knowledge and history state models as local knowledge, and preserve them before forgetting occurs. After local training, we use an adaptive knowledge matrix to fuse knowledge from local, global, and history state models. This fused knowledge is then distilled into an individual model, thus transferring the fused knowledge into global models. Extensive experiments show that pFedAMF consistently outperforms FedAvg, achieving up to 5.22% average accuracy boost, and reducing computation cost by 82% and communication cost by 87%.