EAFL: Towards Energy-Aware Federated Learning on Battery-Powered Edge Devices. (arXiv:2208.04505v1 [cs.LG])

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 …

arxiv battery devices edge edge devices energy federated learning learning lg