  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.
