Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels

arXiv:2412.00452v3 Announce Type: replace Abstract: Conventional federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the federated label-noise (F-LN) problem. Worse still, the F-LN problem is exacerbated by the heterogeneity of FL, whereas clients experience different label-noise types, ratios, and data distribution. In this study, we first observe an intriguing phenomenon that the global model of FL exhibits a slow memorization of noisy labels, suggesting its ability to maintain reliable predictions and robust representatio

Source: arXiv cs.LG — read the full report at the original publisher.

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