Federated Learning with Autonomous Clients on non-IID Data: A Group Collaboration Approach

Personalized federated learning (PFL) has been proposed to overcome the challenge of statistical diversity in clients’ local data distribution under federated scenarios. Some existing PFL methods encourage collaboration between clients to improve overall accuracy. However, these methods often fail to consider the individual preferences of clients; instead, they adopt a top-down decision-making approach to divide clients into disjoint groups. In contrast, we consider the rationality of self-interested clients and allow them to make their own decisions regarding which federation to join. In our proposed PFL framework pFedMGC, we formulate the autonomous decision-making process of clients where they can participate in multiple federations. We then develop a heuristic algorithm to find feasible collaboration in this process. Clients in the same federations collaborate to train a federation-wide global model. To customize these federation models into clients’ personalized models, we employ an adaptive approach to calculate the aggregation weights of federation models based on differences in loss. The experiment results show that pFedMGC can improve overall personalized accuracy up to 28.89%, compared with the state-of-the-art PFL methods.