  Federated noisy label learning (FNLL) is emerging as a promising tool for
privacy-preserving multi-source decentralized learning. Existing research,
relying on the assumption of class-balanced global data, might be incapable to
model complicated label noise, especially in medical scenarios. In this paper,
we first formulate a new and more realistic federated label noise problem where
global data is class-imbalanced and label noise is heterogeneous, and then
propose a two-stage framework named FedNoRo for noise-robust federated
learning. Specifically, in the first stage of FedNoRo, per-class loss
indicators followed by Gaussian Mixture Model are deployed for noisy client
identification. In the second stage, knowledge distillation and a
distance-aware aggregation function are jointly adopted for noise-robust
federated model updating. Experimental results on the widely-used ICH and
ISIC2019 datasets demonstrate the superiority of FedNoRo against the
state-of-the-art FNLL methods for addressing class imbalance and label noise
heterogeneity in real-world FL scenarios.
