Federated learning (FL) is a machine learning methodology that involves the collaborative training of a global model across multiple decentralized clients in a privacy-preserving way. Several FL methods are introduced to tackle communication inefficiencies but do not address how to sample participating clients in each round effectively and in a privacy-preserving manner. In this paper, we propose \textit{FedSTaS}, a client and data-level sampling method inspired by \textit{FedSTS} and \textit{FedSampling}. In each federated learning round, \textit{FedSTaS} stratifies clients based on their compressed gradients, re-allocate the number of clients to sample using an optimal Neyman allocation, and sample local data from each participating clients using a data uniform sampling strategy. Experiments on three datasets show that \textit{FedSTaS} can achieve higher accuracy scores than those of \textit{FedSTS} within a fixed number of training rounds.

翻译：联邦学习（FL）是一种机器学习方法，其以隐私保护的方式在多个去中心化客户端上协作训练全局模型。已有多种联邦学习方法被提出以解决通信效率低下的问题，但未能有效且以隐私保护的方式处理每轮参与客户端的采样问题。本文提出 \textit{FedSTaS}，一种受 \textit{FedSTS} 和 \textit{FedSampling} 启发的客户端与数据级采样方法。在每一轮联邦学习中，\textit{FedSTaS} 根据客户端的压缩梯度对客户端进行分层，通过最优内曼分配重新确定待采样的客户端数量，并使用数据均匀采样策略从每个参与客户端中采样本地数据。在三个数据集上的实验表明，在固定训练轮数内，\textit{FedSTaS} 能够获得比 \textit{FedSTS} 更高的准确率分数。