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，客户端的分布式特性和数据异构性既带来了机遇也带来了挑战。虽然客户端之间的协作能显著增强学习过程，但并非所有协作都有益——某些协作甚至可能产生负面影响。本研究提出了一种新颖算法，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，从而识别出数据分布最有利于特定学习目标的客户端。我们证明，该聚合方法的收敛性能不劣于仅聚合来自相同数据分布客户端的更新方法。此外，实证评估持续表明，由本算法指导的协作优于传统联邦学习方法。这凸显了审慎选择客户端的关键作用，并为未来几年更精简、更高效的联邦学习实现奠定了基础。