Federated Learning (FL) is a distributed machine learning paradigm where clients collaboratively train a model using their local (human-generated) datasets. While existing studies focus on FL algorithm development to tackle data heterogeneity across clients, the important issue of data quality (e.g., label noise) in FL is overlooked. This paper aims to fill this gap by providing a quantitative study on the impact of label noise on FL. We derive an upper bound for the generalization error that is linear in the clients' label noise level. Then we conduct experiments on MNIST and CIFAR-10 datasets using various FL algorithms. Our empirical results show that the global model accuracy linearly decreases as the noise level increases, which is consistent with our theoretical analysis. We further find that label noise slows down the convergence of FL training, and the global model tends to overfit when the noise level is high.

翻译：联邦学习（FL）是一种分布式机器学习范式，其中客户端利用本地（人类生成的）数据集协同训练模型。现有研究主要关注应对客户端间数据异质性的联邦学习算法开发，却忽视了联邦学习中数据质量（如标签噪声）这一重要问题。本文旨在通过量化研究标签噪声对联邦学习的影响来填补这一空白。我们推导出泛化误差的上界，该上界与客户端的标签噪声水平呈线性关系。随后，我们使用多种联邦学习算法在MNIST和CIFAR-10数据集上进行实验。实验结果表明，全局模型精度随噪声水平增加呈线性下降，这与理论分析结果一致。此外，我们发现标签噪声会减缓联邦学习的收敛速度，且在高噪声水平下全局模型易出现过拟合现象。