DPFed: Toward Fair Personalized Federated Learning with Fast Convergence

Instead of training a single global model to fit the needs of all clients, personalized federated learning aims to train multiple client-specific models to better account for data disparities across participating clients. However, existing solutions suffer from serious unfairness among clients in terms of model accuracy and slow convergence under non-IID data. In this paper, we propose a novel personalized federated learning framework, called DPFed, which employs deep reinforcement learning (DRL) to identify relationship between clients and enable closer collaboration among similar clients. By exploiting such relationships, DPFed can personalize model aggregation for each client and achieve fast convergence. Moreover, by regularizing the reward function of DRL, we can reduce the variance of model accuracy across clients and achieve a higher level of fairness. Finally, we conduct extensive experiments to evaluate the effectiveness of our proposed framework under a variety of datasets and degrees of non-IID data distribution. The results demonstrate that DPFed outperforms other alternatives in terms of convergence speed, model accuracy, and fairness.