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.
Tackling the objective inconsistency prob- lem in heterogeneous federated optimization.Advances in neural information processing systems, 33:7611–7623,
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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.