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.

翻译：联邦噪声标签学习（FNLL）正成为保护隐私的多源分布式学习中一种有前景的工具。现有研究基于全局数据类别平衡的假设，可能难以建模复杂的标签噪声，尤其在医疗场景中。本文首先定义了一个更真实的联邦标签噪声新问题，即全局数据存在类别不平衡且标签噪声具有异质性，随后提出了名为FedNoRo的两阶段框架，以实现噪声鲁棒的联邦学习。具体而言，在FedNoRo的第一阶段，采用基于高斯混合模型的逐类损失指标进行噪声客户端识别；在第二阶段，联合使用知识蒸馏与距离感知聚合函数进行噪声鲁棒的联邦模型更新。在广泛使用的ICH和ISIC2019数据集上的实验结果表明，FedNoRo在解决真实联邦学习场景中的类别不平衡与标签噪声异质性问题上显著优于现有最先进的FNLL方法。