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RETRACTED: Federated learning algorithm based on matrix mapping for data privacy over edge computing

Pradyumna Kumar Tripathy (Department of Computer Science and Engineering, Silicon Institute of Technology, Bhubaneswar, India)
Anurag Shrivastava (Department of ECE, Sushila Devi Bansal College, Indore, India)
Varsha Agarwal (Center for Management Studies, Jain University, Bangalore, India)
Devangkumar Umakant Shah (Department of Electrical Engineering, KJ Institute of Engineering and Technology, Vadodara, India)
Chandra Sekhar Reddy L. (Department of CSE, CMR College of Engineering and Technology, Hyderabad, India)
S.V. Akilandeeswari (The Gandhigram Rural Institute Deemed University, Gandhigram, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 14 July 2022

91
This article was retracted on 6 Mar 2024.

Retraction statement

The publishers of the International Journal of Pervasive Computing and Communications wish to retract the article Tripathy, P.K., Shrivastava, A., Agarwal, V., Shah, D.U., L., C.S.R. and Akilandeeswari, S.V. (2022), “Federated learning algorithm based on matrix mapping for data privacy over edge computing”, International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-03-2022-0113

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.

The retracted article is available at: 10.1108/IJPCC-03-2022-0113

Abstract

Purpose

This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.

Design/methodology/approach

In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.

Findings

By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped.

Originality/value

By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.

Keywords

Citation

Tripathy, P.K., Shrivastava, A., Agarwal, V., Shah, D.U., L., C.S.R. and Akilandeeswari, S.V. (2022), "RETRACTED: Federated learning algorithm based on matrix mapping for data privacy over edge computing", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJPCC-03-2022-0113

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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