Comments on “VCD-FL: Verifiable, Collusion-Resistant, and Dynamic Federated Learning”

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

引用次数: 0

Abstract

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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对“VCD-FL：可验证、抗共谋和动态联邦学习”的评论

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。

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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来源期刊

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