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.

为了实现在移动设备上部署联邦学习 (FL) 所必需的加速 FL 收敛，本文提出了一种针对移动设备的残余能量和无线感知的参与者选择 (PS) 设计，称为 REWAFL。REWAFL 引入了一个新颖的 PS 效用函数，综合考虑了全局 FL 训练效用和本地能量效用，将能量消耗和候选移动设备的剩余电池能量进行集成。在所提出的 PS 效用函数框架下，REWAFL 进一步提出了一个针对残余能量和无线感知的本地计算策略，并将陈旧问题的解决方法融入到其效用函数和本地计算策略中。实验结果表明，REWAFL 在提高训练准确性和效率方面是有效的，同时避免了移动设备的 “电池耗尽”。