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

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels arXiv:2412.00452v3 Announce Type: replace-cross 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 stu