Scheduling Algorithms for Federated Learning With Minimal Energy Consumption

نویسندگان

چکیده

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 economic and environmental cost (as also case other techniques). Unfortunately, little work been done optimize energy consumption emissions of carbon dioxide equivalents, as minimization usually left secondary objective. In this paper, we investigate problem minimizing FL by controlling workload distribution. We model Minimal Cost Schedule problem, total with identical, independent, atomic tasks that have be assigned resources arbitrary functions. propose pseudo-polynomial optimal solution based previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem. provide four algorithms scenarios where functions are monotonically increasing follow same behavior. These solutions likewise applicable kinds costs, one-dimensional partition problems.

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ژورنال

عنوان ژورنال: IEEE Transactions on Parallel and Distributed Systems

سال: 2023

ISSN: ['1045-9219', '1558-2183', '2161-9883']

DOI: https://doi.org/10.1109/tpds.2023.3240833