EAFL: Towards Energy-Aware Federated Learning on Battery-Powered Edge Devices

• URL: http://arxiv.org/abs/2208.04505v1
• Date: Tue, 9 Aug 2022 02:15:45 GMT
• Title: EAFL: Towards Energy-Aware Federated Learning on Battery-Powered Edge Devices
• Authors: Amna Arouj and Ahmed M. Abdelmoniem
• Abstract summary: 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. In large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. We develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices.
• Score: 3.448338949969246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. \scheme 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. \scheme improves the testing model accuracy by up to 85\% and decreases the drop-out of clients by up to 2.45$\times$.

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