We address the client-selection problem in federated learning over wireless networks under data heterogeneity. Existing client-selection methods often rely on server-side knowledge of client-specific information, thus compromising privacy. To overcome this issue, we propose a client self-selection strategy based solely on the comparison between locally computed training losses and a centrally updated selection threshold. Furthermore, to support robust aggregation of clients' updates over wireless channels, we integrate this client self-selection strategy into the recently proposed type-based unsourced multiple-access framework over distributed multiple-input multiple-output (D-MIMO) networks. The resulting scheme is completely unsourced: the server does not need to know the identity of the clients. Moreover, no channel state information is required, neither at the clients nor at the server side. Simulation results conducted over a D-MIMO wireless network show that the proposed self-selection strategy matches the performance of a comparable state-of-the-art server-side selection method and consistently outperforms random client selection.

翻译：本文研究了数据异构性下无线网络中联邦学习的客户端选择问题。现有客户端选择方法通常依赖于服务器端对客户端特定信息的掌握，从而可能损害隐私。为克服此问题，我们提出一种仅基于本地计算训练损失与中心更新选择阈值比较的客户端自选择策略。此外，为支持无线信道上客户端更新的鲁棒聚合，我们将该客户端自选择策略集成到最近提出的分布式多输入多输出（D-MIMO）网络中基于类型的无源多址接入框架中。所提出的方案完全无需源标识：服务器无需知晓客户端身份。此外，系统既不需要客户端也不需要服务器端提供信道状态信息。在D-MIMO无线网络上的仿真结果表明，所提出的自选择策略在性能上可匹配同类先进的服务器端选择方法，并始终优于随机客户端选择方案。