AI RESEARCH
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
arXiv CS.LG
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ArXi:2604.28024v1 Announce Type: new Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, modeling label correlations under heterogeneous distributions remains challenging. Due to client-specific label spaces and varying co-occurrence patterns, correlations learned by individual clients inevitably deviate from the global structure, a phenomenon we term label correlation drift.