FedReLa: Imbalanced Federated Learning via Re-Labeling

Federated learning systems struggle when training data is both unevenly distributed across clients and imbalanced within the global dataset. FedReLa addresses this dual challenge through intelligent sample re-labeling, allowing models to correct skewed decision boundaries without needing visibility into the full class distribution across the network. This matters for privacy-critical deployments like healthcare and finance, where decentralized training is mandatory but data heterogeneity remains a stubborn performance bottleneck. The technique opens a path toward more robust federated systems in production environments where extreme class absence is common.

Modelwire context

Explainer

FedReLa's key innovation is that it corrects imbalanced decision boundaries without requiring any client to share class distribution information across the network. Most prior federated learning work either ignores imbalance or requires global visibility into label frequencies, which breaks privacy guarantees.

This connects directly to the June 24 work on synthetic data augmentation for imbalanced classification. That paper showed augmentation doesn't always help and depends on model specification; FedReLa sidesteps the augmentation question entirely by reweighting existing samples through intelligent relabeling. The two papers represent different philosophies for the same bottleneck: one asks when to add synthetic data, the other asks how to extract more signal from what's already there without centralizing information. FedReLa's approach is particularly relevant for settings where augmentation is infeasible due to privacy constraints.

If FedReLa achieves comparable performance to centralized imbalanced learning on standard benchmarks (CIFAR-10-LT, FMNIST-LT) while maintaining strict local-only label access, the method has production viability. If performance degrades significantly on extreme imbalance scenarios (e.g., 100:1 class ratios), it signals the approach trades privacy for accuracy in ways practitioners need to quantify.

Coverage we drew on

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? · arXiv cs.LG

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