arXiv AI August 25, 2026 By Wenxuan Ye, Onur Ayan, Xueli An, Georg Carle

FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

FedCC is a new algorithm for distillation-based federated learning that tackles label distribution skew by allowing clients to mark ambiguous samples as 'unknown' instead of forcing a potentially wrong classification. By adding this extra class and calibrating pseudo-labels on a public dataset, FedCC balances confidence across majority and minority classes. Experiments show that FedCC outperforms existing methods, achieving 67.3% accuracy even when each client has data from only one of ten classes, whereas baselines drop to near-random performance.

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