REWAFL: Residual Energy and Wireless Aware Participant Selection for
  Efficient Federated Learning over Mobile Devices
Y. Li X. Qin J. Geng R. Chen Y. Hou Y. Gong M. Pan P. Zhang
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.