Heterogeneity-Aware Federated Learning for Medical Image Classification With Dynamic Parameter Optimization

Summary

The authors introduce FedDPO, a federated learning approach for medical image classification that uses reinforcement learning to set an adaptive regularization term for each client's objective function. The method accounts for each client's data distribution, system specifications and performance feedback, and adds local batch normalization to reduce the impact of heterogeneous data. Experiments on two medical and one nonmedical image classification tasks show consistent gains over several baselines.