RETRACTED: Energy efficient multi-tasking for edge computing using federated learning
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 8 July 2022
Retraction statement
The publishers of the International Journal of Pervasive Computing and Communications wish to retract the article Soni, M., Nayak, N.R., Kalra, A., Degadwala, S., Singh, N.K. and Singh, S. (2022), “Energy efficient multi-tasking for edge computing using federated learning”, International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-03-2022-0106
An internal investigation into a series of submissions has uncovered evidence that the peer review process was compromised. As a result of these concerns, the findings of the article cannot be relied upon. This decision has been taken in accordance with Emerald's publishing ethics and the COPE guidelines on retractions. The authors of this paper would like to note that they do not agree with the content of this notice. The publishers of the journal sincerely apologize to the readers.
Abstract
Purpose
The purpose of this paper is to improve the existing paradigm of edge computing to maintain a balanced energy usage.
Design/methodology/approach
The new greedy algorithm is proposed to balance the energy consumption in edge computing.
Findings
The new greedy algorithm can balance energy more efficiently than the random approach by an average of 66.59 percent.
Originality/value
The results are shown in this paper which are better as compared to existing algorithms.
Keywords
Citation
Soni, M., Nayak, N.R., Kalra, A., Degadwala, S., Singh, N.K. and Singh, S. (2022), "RETRACTED: Energy efficient multi-tasking for edge computing using federated learning", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-03-2022-0106
Publisher
:Emerald Publishing Limited
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