Energy-Harvesting Distributed Machine Learning

TL;DR

This paper introduces a scalable distributed machine learning framework that leverages energy harvesting devices, providing theoretical guarantees and outperforming traditional energy-agnostic methods in various network settings.

Contribution

It presents the first comprehensive study of energy harvesting for sustainable distributed machine learning with a practical, scalable framework and convergence guarantees.

Findings

Significant performance improvements over energy-agnostic benchmarks

Framework is scalable and requires only local energy estimation

Applicable to wireless networks, edge computing, and IoT

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.