Scheduling Algorithms for Federated Learning With Minimal Energy Consumption

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...

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Bibliographic Details
Published in:	IEEE transactions on parallel and distributed systems Vol. 34; no. 4; pp. 1215 - 1226
Main Author:	Pilla, Laercio Lima
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Artificial Intelligence Biological system modeling Carbon dioxide Computational modeling Computer Science Cost function Costs Data models Data Structures and Algorithms Distributed, Parallel, and Cluster Computing Dynamic programming Electronic devices energy conservation Energy consumption Federated learning knapsack problems Machine learning Optimization parallel processing Polynomials Scheduling Task analysis Training algorithms optimization scheduling energy conservation machine learning parallel processing knapsack problems
ISSN:	1045-9219, 1558-2183
Online Access:	Get full text
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