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

PurposeThis paper aims to provide the security and privacy for Byzantine clients from different types of attacks.Design/methodology/approachIn this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.FindingsBy using Softmax layer probabil...

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Bibliographic Details
Published inInternational journal of pervasive computing and communications Vol. 20; no. 5; pp. 633 - 647
Main Authors Tripathy, Pradyumna Kumar, Shrivastava, Anurag, Agarwal, Varsha, Shah, Devangkumar Umakant, Chandra Sekhar Reddy L, Akilandeeswari, S V
Format Journal Article
LanguageEnglish
Published Bingley Emerald Group Publishing Limited 12.11.2024
Subjects
Aggregates
Algorithms
Clients
Datasets
Edge computing
Experiments
Federated learning
Machine learning
Mapping
Privacy
Probability distribution
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Summary:PurposeThis paper aims to provide the security and privacy for Byzantine clients from different types of attacks.Design/methodology/approachIn this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.FindingsBy 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/valueBy using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.
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ISSN:1742-7371
1742-738X
DOI:10.1108/IJPCC-03-2022-0113