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RETRACTED: Energy efficient multi-tasking for edge computing using federated learning

Mukesh Soni (Department of CSE, University Centre for Research and Development, Chandigarh University, Mohali, India)

Nihar Ranjan Nayak (School of Information Science, Presidency University, Bengaluru, India)

Ashima Kalra (ECE Department, Chandigarh Engineering College, Landran, India)

Sheshang Degadwala (Department of Computer Engineering, Sigma Institute of Engineering, Vadodara, India)

Nikhil Kumar Singh (Department of CSE, Maulana Azad National Institute of Technology, Bhopal, India)

Shweta Singh (Electronics and Communication Department, IES College of Technology, Bhopal, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 8 July 2022

Issue publication date: 30 September 2024

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This article was retracted on 26 Mar 2024.

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. (2024), "RETRACTED: Energy efficient multi-tasking for edge computing using federated learning", International Journal of Pervasive Computing and Communications, Vol. 20 No. 3, pp. 18-32. https://doi.org/10.1108/IJPCC-03-2022-0106

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Emerald Publishing Limited

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RETRACTED: Energy efficient multi-tasking for edge computing using federated learning

Retraction statement

Abstract

Purpose

Design/methodology/approach

Findings

Originality/value

Keywords

Citation

Publisher

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To read this content please select one of the options below:

Please note you do not have access to teaching notes

Retraction statement

Abstract

Purpose

Design/methodology/approach

Findings

Originality/value

Keywords

Citation

Publisher

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