Federated learning (FL) is a decentralized machine learning (ML) technique that enables multiple clients to collaboratively train a common ML model without them having to share their raw data with each other. A typical FL process involves (1) FL client(s) selection, (2) global model distribution, (3) local training, and (4) aggregation. As such FL clients are heterogeneous edge devices (i.e., mobile phones) that differ in terms of computational resources, training data quality, and distribution. Therefore, FL client(s) selection has a significant influence on the execution of the remaining steps of an FL process. There have been a variety of FL client(s) selection models proposed in the literature, however, their critical review and/or comparative analysis is much less discussed. This paper brings the scattered FL client(s) selection models onto a single platform by first categorizing them into five categories, followed by providing a detailed analysis of the benefits/shortcomings and the applicability of these models for different FL scenarios. Such understanding can help researchers in academia and industry to develop improved FL client(s) selection models to address the requirement challenges and shortcomings of the current models. Finally, future research directions in the area of FL client(s) selection are also discussed.

中文翻译：

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联邦学习中的客户选择模型综述

联邦学习 (FL) 是一种去中心化机器学习 (ML) 技术，使多个客户端能够协作训练通用 ML 模型，而无需彼此共享原始数据。典型的 FL 过程涉及 (1) FL 客户端选择、(2) 全局模型分发、(3) 本地训练和 (4) 聚合。因此，FL 客户端是异构边缘设备（即移动电话），它们在计算资源、训练数据质量和分布方面有所不同。因此，FL 客户端的选择对 FL 过程的其余步骤的执行具有重大影响。文献中提出了多种 FL 客户选择模型，但是，对其批判性评论和/或比较分析的讨论却少得多。本文将分散的 FL 客户选择模型引入单一平台，首先将其分为五类，然后详细分析这些模型的优点/缺点以及这些模型对于不同 FL 场景的适用性。这种理解可以帮助学术界和工业界的研究人员开发改进的 FL 客户端选择模型，以解决当前模型的需求挑战和缺点。最后，还讨论了 FL 客户选择领域的未来研究方向。