  Potential environmental impact of machine learning by large-scale wireless
networks is a major challenge for the sustainability of future smart
ecosystems. In this paper, we introduce sustainable machine learning in
federated learning settings, using rechargeable devices that can collect energy
from the ambient environment. We propose a practical federated learning
framework that leverages intermittent energy arrivals for training, with
provable convergence guarantees. Our framework can be applied to a wide range
of machine learning settings in networked environments, including distributed
and federated learning in wireless and edge networks. Our experiments
demonstrate that the proposed framework can provide significant performance
improvement over the benchmark energy-agnostic federated learning settings.
