Federated Learning (FL) allows several clients to construct a common global machine-learning model without having to share their data. FL, however, faces the challenge of statistical heterogeneity between the client's data, which degrades performance and slows down the convergence toward the global model. In this paper, we provide theoretical proof that minimizing heterogeneity between clients facilitates the convergence of a global model for every single client. This becomes particularly important under empirical concept shifts among clients, rather than merely considering imbalanced classes, which have been studied until now. Therefore, we propose a method for knowledge transfer between clients where the server trains client-specific generators. Each generator generates samples for the corresponding client to remove the conflict with other clients' models. Experiments conducted on synthetic and real data, along with a theoretical study, support the effectiveness of our method in constructing a well-generalizable global model by reducing the conflict between local models.

翻译：联邦学习（FL）允许多个客户端在不共享数据的情况下构建共同的全局机器学习模型。然而，FL面临客户端数据之间的统计异质性挑战，这会降低性能并减缓全局模型的收敛速度。本文从理论上证明，最小化客户端之间的异质性有助于每个客户端全局模型的收敛。这一点在客户端之间存在经验性概念漂移时尤为重要，而非仅仅考虑此前研究中不平衡类别的分布。因此，我们提出一种客户端间的知识迁移方法：服务器训练客户端专属生成器。每个生成器为对应客户端生成样本，以消除与其他客户端模型的冲突。基于合成数据与真实数据的实验及理论研究均支持本方法的有效性——它能通过减少局部模型间的冲突，构建具有良好泛化能力的全局模型。