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 in | International journal of pervasive computing and communications Vol. 20; no. 3; pp. 18 - 32 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Bingley
Emerald Group Publishing Limited
30.09.2024
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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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| AbstractList | 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. |
| Author | Degadwala, Sheshang Singh, Shweta Kalra, Ashima Singh, Nikhil Kumar Nayak, Nihar Ranjan Soni, Mukesh |
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| DOI | 10.1108/IJPCC-03-2022-0106 |
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| SubjectTerms | 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 |
| Title | Energy efficient multi-tasking for edge computing using federated learning |
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