Energy efficient multi-tasking for edge computing using federated learning

PurposeThe purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.Design/methodology/approachThe new greedy algorithm is proposed to balance the energy consumption in edge computing.FindingsThe new greedy algorithm can balance energy more effi...

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Published inInternational journal of pervasive computing and communications Vol. 20; no. 3; pp. 18 - 32
Main Authors Soni, Mukesh, Nayak, Nihar Ranjan, Kalra, Ashima, Degadwala, Sheshang, Singh, Nikhil Kumar, Singh, Shweta
Format Journal Article
LanguageEnglish
Published Bingley Emerald Group Publishing Limited 30.09.2024
Subjects
Cloud computing
Data processing
Datasets
Decision making
Decomposition
Deep learning
Edge computing
Energy consumption
Federated learning
Greedy algorithms
Internet of Things
Machine learning
Multitasking
Privacy
Semantics
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Summary:PurposeThe purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.Design/methodology/approachThe new greedy algorithm is proposed to balance the energy consumption in edge computing.FindingsThe new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.Originality/valueThe results are shown in this paper which are better as compared to existing algorithms.
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ISSN:1742-7371
1742-738X
DOI:10.1108/IJPCC-03-2022-0106