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

This research addresses challenges in federated learning (FL, a method where multiple institutions train an AI model together without sharing private data) by introducing FedDPO, which uses reinforcement learning (a type of AI that learns through trial and error feedback) to automatically adjust regularization terms (mathematical penalties that stabilize training) for each participant based on their unique data and system conditions. The approach also uses local batch normalization (a technique that normalizes data within each institution) to handle differences in how data is distributed across institutions, and testing on medical image classification tasks shows it outperforms existing methods.