Sustainable Federated Learning

Basak Guler; Aylin Yener

Sustainable Federated Learning

Machine Learning 2021-02-23 v1 Information Theory math.IT

Authors: Basak Guler , Aylin Yener

Abstract

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.

Keywords

federated learning green computing machine learning

Cite

@article{arxiv.2102.11274,
  title  = {Sustainable Federated Learning},
  author = {Basak Guler and Aylin Yener},
  journal= {arXiv preprint arXiv:2102.11274},
  year   = {2021}
}

Comments

12 pages, 1 figure

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