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一个共同的全局机器学习模型。然而，FL面临客户端数据统计异质性的挑战，这会降低性能并减缓向全局模型收敛的速度。本文提供了理论证明，表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出现经验概念漂移时尤为重要，而不仅仅是考虑截至目前已研究的类别不平衡问题。因此，我们提出了一种客户端间的知识迁移方法，其中服务器训练客户端特定的生成器。每个生成器为相应客户端生成样本，以消除与其他客户端模型的冲突。基于合成数据和真实数据的实验以及理论研究支持了我们的方法在通过减少局部模型之间的冲突来构建良好泛化全局模型方面的有效性。