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.
Generaliz- able heterogeneous federated cross-correlation and instance similarity learning.IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(2):712–728
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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.