Federated learning is an emerging machine learning paradigm that enables devices to train collaboratively without exchanging their local data. The clients participating in the training process are a random subset selected from the pool of clients. The above procedure is called client selection which is an important area in federated learning as it highly impacts the convergence rate, learning efficiency, and generalization. In this work, we introduce client filtering in federated learning (FilFL), a new approach to optimize client selection and training. FilFL first filters the active clients by choosing a subset of them that maximizes a specific objective function; then, a client selection method is applied to that subset. We provide a thorough analysis of its convergence in a heterogeneous setting. Empirical results demonstrate several benefits to our approach, including improved learning efficiency, accelerated convergence, $2$-$3\times$ faster, and higher test accuracy, around $2$-$10$ percentage points higher.

翻译：联邦学习是一种新兴的机器学习范式，使设备能够在不交换本地数据的情况下协同训练。参与训练过程的客户端是从客户端池中随机选取的子集。上述流程称为客户端选择，是联邦学习中的重要研究领域，因其对收敛速度、学习效率和泛化性能具有显著影响。本文提出联邦学习中的客户端过滤方法（FilFL），一种用于优化客户端选择与训练的新方案。FilFL首先通过选择最大化特定目标函数的客户端子集来过滤活跃客户端；随后对该子集应用客户端选择方法。我们对其在异构环境下的收敛性进行了全面分析。实验结果表明，该方法具有多重优势，包括提高学习效率、加速收敛（速度提升2-3倍）以及提升测试准确率（约提高2-10个百分点）。