Energy-Harvesting Distributed Machine Learning
Energy-Harvesting Distributed Machine Learning
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DOI：
10.1109/isit45174.2021.9518045
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发表时间：
2021-02
期刊：
影响因子：
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通讯作者：
Basak Guler;A. Yener
中科院分区：
文献类型：
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作者：
Basak Guler;A. Yener
本文提供了在分布式网络中利用能量收集进行可持续机器学习的第一项研究。我们考虑了一个分布式学习设置，其中机器学习模型在大量可以从周围环境中获取能量的设备上进行训练，并开发了一个具有理论收敛保证的实用学习框架。我们通过数值实验表明，所提出的框架可以显着超越能量不可知的基准。我们的框架是可扩展的，只需要对能量统计数据进行本地估计，并且可以应用于广泛的分布式训练设置，包括无线网络中的机器学习，边缘计算和移动的物联网。
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 out- perform 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.