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Federated noisy label learning (FNLL) is emerging as a promising tool forprivacy-preserving multi-source decentralized learning. Existing research,relying on the assumption of class-balanced global data, might be incapable tomodel complicated label noise, especially in medical scenarios. In this paper,we first formulate a new and more realistic federated label noise problem whereglobal data is class-imbalanced and label noise is heterogeneous, and thenpropose a two-stage framework named FedNoRo for noise-robust federatedlearning. Specifically, in the first stage of FedNoRo, per-class lossindicators followed by Gaussian Mixture Model are deployed for noisy clientidentification. In the second stage, knowledge distillation and adistance-aware aggregation function are jointly adopted for noise-robustfederated model updating. Experimental results on the widely-used ICH andISIC2019 datasets demonstrate the superiority of FedNoRo against thestate-of-the-art FNLL methods for addressing class imbalance and label noiseheterogeneity in real-world FL scenarios.

FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity