Federated Learning (FL) enables decentralized training of machine learning models on distributed data while preserving privacy. However, in real-world FL settings, client data is often non-identically distributed and imbalanced, resulting in statistical data heterogeneity which impacts the generalization capabilities of the server's model across clients, slows convergence and reduces performance. In this paper, we address this challenge by first proposing a characterization of statistical data heterogeneity by means of 6 metrics of global and client attribute imbalance, class imbalance, and spurious correlations. Next, we create and share 7 computer vision datasets for binary and multiclass image classification tasks in Federated Learning that cover a broad range of statistical data heterogeneity and hence simulate real-world situations. Finally, we propose FedDiverse, a novel client selection algorithm in FL which is designed to manage and leverage data heterogeneity across clients by promoting collaboration between clients with complementary data distributions. Experiments on the seven proposed FL datasets demonstrate FedDiverse's effectiveness in enhancing the performance and robustness of a variety of FL methods while having low communication and computational overhead.

本研究解决了联邦学习中因客户端数据的非独立同分布和不平衡性而导致的统计数据异质性问题，影响模型的泛化能力。通过提出6种量化指标来表征数据异质性，创建7个适用于二元和多类图像分类任务的数据集，并提出了一种新颖的客户端选择算法FedDiverse，研究表明该方法能够显著提升联邦学习模型的性能和鲁棒性，同时保持低通信和计算开销。

基于多样性的学习：解决联邦模型中的虚假相关性和数据异质性问题