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Towards Energy-Aware Federated Learning on Battery-Powered Clients. (arXiv:2208.04505v2 [cs.LG] UPDATED)
Aug. 26, 2022, 1:11 a.m. | Amna Arouj, Ahmed M. Abdelmoniem
cs.LG updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.LG updates on arXiv.org
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