FedHarmony harmonizes heterogeneous label correlations in federated multi-label learning via consensus correlations as global teachers and quality-weighted aggregation, with an accelerated optimizer that converges faster while improving accuracy over prior methods.
Privacy- preserving multi-label propagation based on federated learn- ing.IEEE Transactions on Network Science and Engineer- ing, 11(1):886–899
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FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
FedHarmony harmonizes heterogeneous label correlations in federated multi-label learning via consensus correlations as global teachers and quality-weighted aggregation, with an accelerated optimizer that converges faster while improving accuracy over prior methods.