FedReLa

FedReLa: Re-labeling approach for imbalanced federated learning under data heterogeneity

Researchers propose FedReLa, a data-level method for federated learning that addresses the coexistence of global class imbalance and cross-client data heterogeneity. The approach uses a feature-dependent label re-allocator to correct biased global decision boundaries without requiring knowledge of the global class distribution. FedReLa is model-agnostic and modular, integrating with existing algorithmic methods without additional communication overhead, and claims state-of-the-art results on stepwise-imbalanced and long-tailed datasets.