对“VCD-FL:可验证、抗共谋和动态联邦学习”的评论

IF 8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS IEEE Transactions on Information Forensics and Security Pub Date : 2025-01-23 DOI:10.1109/TIFS.2025.3533141
Zhuoqun Yan;Wenfang Zhang;Xiaomin Wang;Muhammad Khurram Khan
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引用次数: 0

摘要

Gao等人(2023)最近提出了一个名为VCD-FL的抗共谋和可验证的联邦学习框架(IEEE TRANSACTIONS ON INFORMATION FORENSICS and SECURITY, vol. 18, pp. 3760-3773, 2023)。然而,在这封信中,我们表明VCD-FL未能实现其声称的安全目标。特别是,我们证明了他们设计的承诺方案是不安全的,作为所提出的抗合谋验证机制的核心组件,然后我们提出了一种可行的合谋攻击,由聚合服务器和破坏客户端利用现有的安全漏洞发起。
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Comments on “VCD-FL: Verifiable, Collusion-Resistant, and Dynamic Federated Learning”
Gao et al. (2023)recently proposed a collusion-resistant and verifiable federated learning framework named VCD-FL (IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, vol. 18, pp. 3760–3773, 2023). However, in this letter, we show that VCD-FL fails to achieve its claimed security goals. In particular, we demonstrate that their designed commitment scheme, which serves as the core component of the proposed collusion-resistant verification mechanism, is unsafe, and then we present a feasible collusion attack launched by the aggregation server and corrupt clients by leveraging the existing security vulnerabilities.
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来源期刊
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security 工程技术-工程:电子与电气
CiteScore
14.40
自引率
7.40%
发文量
234
审稿时长
6.5 months
期刊介绍: The IEEE Transactions on Information Forensics and Security covers the sciences, technologies, and applications relating to information forensics, information security, biometrics, surveillance and systems applications that incorporate these features
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