基于矩阵映射的边缘计算数据隐私联合学习算法

IF 0.6 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

International Journal of Pervasive Computing and Communications Pub Date : 2022-07-14 DOI:10.1108/ijpcc-03-2022-0113

P. Tripathy, Anurag Shrivastava, Varsha Agarwal, Devangkumar Umakant Shah, C. L, S. .. Akilandeeswari

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引用次数: 1

摘要

目的本文旨在为拜占庭客户端提供不同类型攻击的安全性和隐私性。设计/方法论/方法在本文中，作者使用基于矩阵映射的联合学习算法来保护边缘计算上的数据隐私。通过使用Softmax层概率分布，模型拜占庭容忍度可以在阻塞收敛攻击中从40%提高到45%，并且可以阻止边缘后门攻击。独创性/价值通过使用Softmax层概率分布对测试结果进行建模，聚合方法可以保护至少30%的拜占庭客户端。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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.

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来源期刊

International Journal of Pervasive Computing and Communications COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

6.60

自引率

0.00%

发文量