  Federated learning (FL) is a newly emerged branch of AI that facilitates edge
devices to collaboratively train a global machine learning model without
centralizing data and with privacy by default. However, despite the remarkable
advancement, this paradigm comes with various challenges. Specifically, in
large-scale deployments, client heterogeneity is the norm which impacts
training quality such as accuracy, fairness, and time. Moreover, energy
consumption across these battery-constrained devices is largely unexplored and
a limitation for wide-adoption of FL. To address this issue, we develop EAFL,
an energy-aware FL selection method that considers energy consumption to
maximize the participation of heterogeneous target devices. EAFL is a
power-aware training algorithm that cherry-picks clients with higher battery
levels in conjunction with its ability to maximize the system efficiency. Our
design jointly minimizes the time-to-accuracy and maximizes the remaining
on-device battery levels. EAFLimproves the testing model accuracy by up to 85\%
and decreases the drop-out of clients by up to 2.45$\times$.
