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Technical Program

Paper Detail

Paper IDD1-S5-T3.2
Paper Title Energy-Harvesting Distributed Machine Learning
Authors Basak Guler, University of California, Riverside, United States; Aylin Yener, The Ohio State University, United States
Session D1-S5-T3: Distributed Learning
Chaired Session: Monday, 12 July, 23:20 - 23:40
Engagement Session: Monday, 12 July, 23:40 - 00:00
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.