arXiv stat.ML
· Papers
FedReLa: Imbalanced Federated Learning via Re-Labeling
arXiv:2606.26037v1 Announce Type: new Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation. The global class imbalance and cross-client data heterogeneity naturally coexist, and the mismatch between local and global imbalances exacerbates the perfo