Participant selection (PS) helps to accelerate federated learning (FL) convergence, which is essential for the practical deployment of FL over mobile devices. While most existing PS approaches focus on improving training accuracy and efficiency rather than residual energy of mobile devices, which fundamentally determines whether the selected devices can participate. Meanwhile, the impacts of mobile devices' heterogeneous wireless transmission rates on PS and FL training efficiency are largely ignored. Moreover, PS causes the staleness issue. Prior research exploits isolated functions to force long-neglected devices to participate, which is decoupled from original PS designs. In this paper, we propose a residual energy and wireless aware PS design for efficient FL training over mobile devices (REWAFL). REW AFL introduces a novel PS utility function that jointly considers global FL training utilities and local energy utility, which integrates energy consumption and residual battery energy of candidate mobile devices. Under the proposed PS utility function framework, REW AFL further presents a residual energy and wireless aware local computing policy. Besides, REWAFL buries the staleness solution into its utility function and local computing policy. The experimental results show that REW AFL is effective in improving training accuracy and efficiency, while avoiding "flat battery" of mobile devices.

翻译：参与者选择（PS）有助于加速联邦学习（FL）收敛，这对于FL在移动设备上的实际部署至关重要。然而，现有的大多数PS方法侧重于提升训练精度和效率，而非移动设备的剩余能量——而剩余能量从根本上决定了所选设备能否参与训练。同时，移动设备异构无线传输速率对PS及FL训练效率的影响在很大程度上被忽视。此外，PS还会引发陈旧性问题。现有研究利用孤立的函数强制长期被忽视的设备参与训练，这一做法与原始PS设计相脱节。本文提出了一种面向移动设备高效FL训练的残能量与无线感知参与者选择设计（REWAFL）。REWAFL引入了一种新颖的PS效用函数，该函数联合考虑全局FL训练效用和局部能量效用，并整合了候选移动设备的能耗和剩余电池能量。在所提出的PS效用函数框架下，REWAFL进一步提出了一种残能量与无线感知的本地计算策略。此外，REWAFL将陈旧性问题解决方案内嵌至其效用函数及本地计算策略中。实验结果表明，REWAFL能有效提升训练精度和效率，同时避免移动设备出现“电池耗尽”问题。