Towards Feature-Consistent Parameter Collaboration for Personalized Federated Learning

Abstract

Personalized federated learning (PFL) aims to improve the performance of the local model on each client with the non-IID data among different clients. This paper introduces FedFPC, a PFL method that allows effective and robust parameter-wise collaboration to achieve outperforming performance. Stem from the idea that similar clients should share more consistent feature representation and benefit more from each other, two strategies are designed to ensure feature consistency during training. First, we present an Attention-Guided Critical Parameter Selection strategy for critical parameter selection, which utilizes the attention prior from current expressive all-purpose features to identify the parameter with the most contribution to feature representation for the local data. Then, a Feature-Consistent Parameter Collaboration strategy is proposed to provide robust parameter collaboration for the local model with help from feature-consistent clients, which obtain similar feature representations to the target client. Experimental results demonstrate that FedFPC stands out by its superior performance in various PFL tasks compared to state-of-the-art methods, meanwhile with better robustness in diverse complex scenarios.