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（Federated Learning, FL）是一种分布式机器学习范式，其中客户端利用其本地（由人类生成的）数据集协作训练模型。现有研究主要关注解决客户端间数据异质性的FL算法开发，却忽视了FL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FL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FL算法在MNIST和CIFAR-10数据集上进行了实验。实证结果表明，全局模型准确率随噪声水平增加而线性下降，这与我们的理论分析一致。我们进一步发现，标签噪声会减缓FL训练的收敛速度，且当噪声水平较高时，全局模型容易出现过拟合。