Federated learning (FL) enables collaborative model training without sharing raw data; however, the presence of noisy labels across distributed clients can severely degrade the learning performance. In this paper, we propose FedSIR, a multi-stage framework for robust FL under noisy labels. Different from existing approaches that mainly rely on designing noise-tolerant loss functions or exploiting loss dynamics during training, our method leverages the spectral structure of client feature representations to identify and mitigate label noise. Our framework consists of three key components. First, we identify clean and noisy clients by analyzing the spectral consistency of class-wise feature subspaces with minimal communication overhead. Second, clean clients provide spectral references that enable noisy clients to relabel potentially corrupted samples using both dominant class directions and residual subspaces. Third, we employ a noise-aware training strategy that integrates logit-adjusted loss, knowledge distillation, and distance-aware aggregation to further stabilize federated optimization. Extensive experiments on standard FL benchmarks demonstrate that FedSIR consistently outperforms state-of-the-art methods for FL with noisy labels. The code is available at https://github.com/sinagh72/FedSIR.

翻译：联邦学习（FL）能够在无需共享原始数据的情况下实现协作模型训练，然而分布式客户端中存在的噪声标签会严重降低学习性能。本文提出FedSIR——一种用于含噪标签环境下鲁棒联邦学习的多阶段框架。与现有主要依赖设计噪声容忍损失函数或利用训练过程中损失动态的方法不同，我们的方法利用客户端特征表示的频谱结构来识别并缓解标签噪声。该框架包含三个关键组成部分：首先，通过分析类级特征子空间的频谱一致性，以最小通信开销识别干净与噪声客户端；其次，干净客户端提供频谱参考，使得噪声客户端能够利用主导类方向与残差子空间对可能受污染的样本进行重标定；最后，采用噪声感知训练策略，融合对数调整损失、知识蒸馏与距离感知聚合以进一步稳定联邦优化过程。在标准FL基准上的大量实验表明，FedSIR在含噪标签联邦学习中持续优于现有最优方法。代码已开源于https://github.com/sinagh72/FedSIR。