FedGR rectifies noisy labels in federated learning via global revision modules that exploit slow memorization, outperforming seven baselines on F-LN benchmarks under heterogeneity.
Tsang, and Masashi Sugiyama
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Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels
FedGR rectifies noisy labels in federated learning via global revision modules that exploit slow memorization, outperforming seven baselines on F-LN benchmarks under heterogeneity.