Federated learning is an emerging machine learning paradigm that enables clients to train collaboratively without exchanging local data. The clients participating in the training process have a crucial impact on the convergence rate, learning efficiency, and model generalization. In this work, we propose FilFL, a new approach to optimizing client participation and training by introducing client filtering. FilFL periodically filters the available clients to identify a subset that maximizes a combinatorial objective function using an efficient greedy filtering algorithm. From this filtered-in subset, clients are then selected for the training process. We provide a thorough analysis of FilFL convergence in a heterogeneous setting and evaluate its performance across diverse vision and language tasks and realistic federated scenarios with time-varying client availability. Our empirical results demonstrate several benefits of our approach, including improved learning efficiency, faster convergence, and up to 10 percentage points higher test accuracy compared to scenarios where client filtering is not utilized.

翻译：联邦学习是一种新兴的机器学习范式，它使得客户端无需交换本地数据即可协同训练。参与训练过程的客户端对收敛速度、学习效率和模型泛化能力具有关键影响。本研究提出FilFL——一种通过引入客户端过滤机制来优化客户端参与和训练过程的新方法。FilFL采用高效的贪心过滤算法，定期对可用客户端进行过滤，筛选出能最大化组合目标函数的客户端子集，并从中选取客户端参与训练流程。我们深入分析了异构场景下FilFL的收敛性，并在不同视觉与语言任务以及具有时变客户端可用性的现实联邦场景中评估其性能。实验结果表明，与未采用客户端过滤的方法相比，我们的方法在多个方面具有优势，包括：提升学习效率、加速收敛，以及测试准确率最高可提升10个百分点。