Federated learning (FL) enables collaboratively training a joint model for multiple medical centers, while keeping the data decentralized due to privacy concerns. However, federated optimizations often suffer from the heterogeneity of the data distribution across medical centers. In this work, we propose Federated Learning with Shared Label Distribution (FedSLD) for classification tasks, a method that adjusts the contribution of each data sample to the local objective during optimization via knowledge of clients' label distribution, mitigating the instability brought by data heterogeneity. We conduct extensive experiments on four publicly available image datasets with different types of non-IID data distributions. Our results show that FedSLD achieves better convergence performance than the compared leading FL optimization algorithms, increasing the test accuracy by up to 5.50 percentage points.
Keywords: Classification; Federated Learning; Medical imaging; Prior distribution.