In Federated Learning (FL), the distributed nature and heterogeneity of client data present both opportunities and challenges. While collaboration among clients can significantly enhance the learning process, not all collaborations are beneficial; some may even be detrimental. In this study, we introduce a novel algorithm that assigns adaptive aggregation weights to clients participating in FL training, identifying those with data distributions most conducive to a specific learning objective. We demonstrate that our aggregation method converges no worse than the method that aggregates only the updates received from clients with the same data distribution. Furthermore, empirical evaluations consistently reveal that collaborations guided by our algorithm outperform traditional FL approaches. This underscores the critical role of judicious client selection and lays the foundation for more streamlined and effective FL implementations in the coming years.

翻译：在联邦学习中，客户端数据的分布特性与异质性既带来了机遇也构成了挑战。虽然客户端间的协作能显著提升学习过程，但并非所有协作都有益——有些甚至可能产生负面影响。本研究提出了一种新型算法，该算法为参与联邦学习训练的客户端分配自适应聚合权重，能够识别出对特定学习目标最有利的数据分布特征。理论证明该聚合方法的收敛性不劣于仅聚合来自相同数据分布客户端更新的方法。此外，实证评估一致表明，基于该算法指导的协作性能优于传统联邦学习方法。这项研究凸显了审慎选择客户端的关键作用，为未来更高效精简的联邦学习实现奠定了重要基础。