In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across clients. Traditional centralized learning approaches to mitigate label noise are constrained in FL by privacy concerns and the heterogeneity of client data. This paper revisits early-learning regularization, introducing an innovative strategy, Federated Label-mixture Regularization (FLR). FLR adeptly adapts to FL's complexities by generating new pseudo labels, blending local and global model predictions. This method not only enhances the accuracy of the global model in both i.i.d. and non-i.i.d. settings but also effectively counters the memorization of noisy labels. Demonstrating compatibility with existing label noise and FL techniques, FLR paves the way for improved generalization in FL environments fraught with label inaccuracies.

翻译：在联邦学习（FL）不断发展的背景下，由于跨客户端数据收集的去中心化和多样性，标签噪声带来了独特的挑战。传统的集中式学习缓解标签噪声的方法在FL中受到隐私问题和客户端数据异质性的限制。本文重新审视早期学习正则化，引入了一种创新策略——联邦标签混合正则化（FLR）。FLR通过生成新的伪标签，融合局部和全局模型预测，巧妙地适应了FL的复杂性。该方法不仅在独立同分布和非独立同分布设置下提升了全局模型的准确性，还有效抑制了对噪声标签的记忆。FLR展示了与现有标签噪声和FL技术的兼容性，为在充满标签错误的FL环境中提升泛化能力铺平了道路。