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Machine learning in medical research, by nature, needs careful attention onobeying the regulations of data privacy, making it difficult to train a machinelearning model over gathered data from different medical centers. Failure ofleveraging data of the same kind may result in poor generalizability for thetrained model. Federated learning (FL) enables collaboratively training a jointmodel while keeping the data decentralized for multiple medical centers.However, federated optimizations often suffer from the heterogeneity of thedata distribution across medical centers. In this work, we propose FederatedLearning with Shared Label Distribution (FedSLD) for classification tasks, amethod that assumes knowledge of the label distributions for all theparticipating clients in the federation. FedSLD adjusts the contribution ofeach data sample to the local objective during optimization given knowledge ofthe distribution, mitigating the instability brought by data heterogeneityacross all clients. We conduct extensive experiments on four publicly availableimage datasets with different types of non-IID data distributions. Our resultsshow that FedSLD achieves better convergence performance than the comparedleading FL optimization algorithms, increasing the test accuracy by up to 5.50percentage points.

FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification