Mitigating Catastrophic Forgetting in Federated Learning for Heterogeneous Medical Image Classification

The proposed FedImpres method alleviates catastrophic forgetting in federated learning by generating synthetic data that captures the global information learned by the aggregated server model, and using it to regularize local training on heterogeneous client data.