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FedReLa: Imbalanced Federated Learning via Re-Labeling

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arXiv:2606. 26037v1 Announce Type: new Abstract: Federated learning has emerged as the foremost approach for decentralized model training with privacy preservation.

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Authors: Guangzheng Hu, Patricia Men\'endez, Feng Liu, Mingming Gong, Guanghui Wang, Liuhua Peng
Released: 2026-06-25 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because FedReLa: Imbalanced Federated Learning via Re-Labeling overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Coupling evil personas with wrong answers fails to protect Qwen2.5-7B from EM-induced alignment collapse — and the apparent capability ordering across coupling conditions is mostly eval contamination (LOW confidence)". Matching terms: class, rect, under, correct, rate, without, model. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.26037

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