Scheduling Algorithms for Federated Learning with Minimal Energy Consumption. (arXiv:2209.06210v1 [cs.LG])

Federated Learning (FL) has opened the opportunity for collaboratively
training machine learning models on heterogeneous mobile or Edge devices while
keeping local data private.With an increase in its adoption, a growing concern
is related to its economic and environmental cost (as is also the case for
other machine learning techniques).Unfortunately, little work has been done to
optimize its energy consumption or emissions of carbon dioxide or equivalents,
as energy minimization is usually left as a secondary objective.In this paper,
we …

algorithms arxiv energy federated learning scheduling