GRACE: A Generalized and Personalized Federated Learning Method for Medical Imaging

Federated learning has been extensively explored in privacy-preserving medical image analysis. However, the domain shift widely existed in real-world scenarios still greatly limits its practice, which requires to consider both generalization and personalization, namely generalized and personalized federated learning (GPFL). Previous studies almost focus on the partial objective of GPFL: personalized federated learning mainly cares about its local performance, which cannot guarantee a generalized global model for unseen clients; federated domain generalization only considers the out-of-domain performance, ignoring the performance of the training clients. To achieve both objectives effectively, we propose a novel GRAdient CorrEction (GRACE) method. GRACE incorporates a feature alignment regularization under a meta-learning framework on the client side to correct the personalized gradients from overfitting. Simultaneously, GRACE employs a consistency-enhanced re-weighting aggregation to calibrate the uploaded gradients on the server side for better generalization. Extensive experiments on two medical image benchmarks demonstrate the superiority of our method under various GPFL settings. Code available at https://github.com/MediaBrain-SJTU/GPFL-GRACE.