Energy-Harvesting Distributed Machine Learning

• URL: http://arxiv.org/abs/2102.05639v1
• Date: Wed, 10 Feb 2021 18:53:51 GMT
• Title: Energy-Harvesting Distributed Machine Learning
• Authors: Basak Guler, Aylin Yener
• Abstract summary: We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings.
• Score: 38.5300206965018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment, and develop a practical learning framework with theoretical convergence guarantees. We demonstrate through numerical experiments that the proposed framework can significantly outperform energy-agnostic benchmarks. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings, including machine learning in wireless networks, edge computing, and mobile internet of things.

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