FedMed-GAN: Federated domain translation on unsupervised cross-modality brain image synthesis

Jinbao Wang, Guoyang Xie, Yawen Huang, Jiayi Lyu, Feng Zheng, Yefeng Zheng, Yaochu Jin

Abstract: Highlights•FedMed-GAN, to the best of our knowledge, is the first work to establish a new benchmark for federally cross-modality brain image synthesis, which greatly facilitates the development of medical GAN with differential privacy guarantees.•We provide comprehensive explanations for treating mode collapse and performance drop compared to centralized training.•The proposed work simulates as much as possible proportions of unpaired and paired data for each client with various data distributions for all clients. The performance of FedMed-GAN remains stable when facing long-tail data distributions.