CoBA: Collusive Backdoor Attacks With Optimized Trigger to Federated Learning

Considerable efforts have been devoted to addressing distributed backdoor attacks in federated learning (FL) systems. While significant progress has been made in enhancing the security of FL systems, our study reveals that there remains a false sense of security surrounding FL. We demonstrate that colluding malicious participants can effectively execute backdoor attacks during the FL training process, exhibiting high sparsity and stealthiness, which means they can evade common defense methods with only a few attack iterations. Our research highlights this vulnerability by proposing a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b>llusive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b>ackdoor <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>ttack named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i>. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> is designed to enhance the sparsity and stealthiness of backdoor attacks by offering trigger tuning to facilitate learning of backdoor training data, controlling the bias of malicious local model updates, and applying the projected gradient descent technique. By conducting extensive empirical studies on 5 benchmark datasets, we make the following observations: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> successfully circumvents 15 state-of-the-art defense methods for robust FL; 2) Compared to existing backdoor attacks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> consistently achieves superior attack performance; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> can achieve persistent poisoning effects through significantly sparse attack iterations. These findings raise substantial concerns regarding the integrity of FL and underscore the urgent need for heightened vigilance in defending against such attacks.

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Publication Details

Journal
IEEE Transactions on Dependable and Secure Computing
Published
2024-08-19
DOI
https://doi.org/10.1109/tdsc.2024.3445637
Citations
13
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
4.52
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article

CoBA: Collusive Backdoor Attacks With Optimized Trigger to Federated Learning

Xiangliang Zhang, Yidong Li, Jingkai Liu, Jiqiang Liu et al.
13 citations
IEEE Transactions on Dependable and Secure Computing
Adversarial Robustness in Machine Learning
4.52
article

CoBA: Collusive Backdoor Attacks With Optimized Trigger to Federated Learning

Xiangliang Zhang, Yidong Li, Jingkai Liu, Jiqiang Liu, Yufei Han, Xiaoting Lyu, Kai Chen, Wei Wang, Bin Wang
article en
13 citations

Abstract

Considerable efforts have been devoted to addressing distributed backdoor attacks in federated learning (FL) systems. While significant progress has been made in enhancing the security of FL systems, our study reveals that there remains a false sense of security surrounding FL. We demonstrate that colluding malicious participants can effectively execute backdoor attacks during the FL training process, exhibiting high sparsity and stealthiness, which means they can evade common defense methods with only a few attack iterations. Our research highlights this vulnerability by proposing a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co</b>llusive <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">B</b>ackdoor <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b>ttack named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i>. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> is designed to enhance the sparsity and stealthiness of backdoor attacks by offering trigger tuning to facilitate learning of backdoor training data, controlling the bias of malicious local model updates, and applying the projected gradient descent technique. By conducting extensive empirical studies on 5 benchmark datasets, we make the following observations: 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> successfully circumvents 15 state-of-the-art defense methods for robust FL; 2) Compared to existing backdoor attacks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> consistently achieves superior attack performance; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoBA</i> can achieve persistent poisoning effects through significantly sparse attack iterations. These findings raise substantial concerns regarding the integrity of FL and underscore the urgent need for heightened vigilance in defending against such attacks.

IEEE Transactions on Dependable and Secure ComputingVol. 22(2)
University of Notre Dame (US), Institut national de recherche en sciences et technologies du numérique (FR), Chinese Academy of Sciences (CN), Beijing Jiaotong University (CN), Zhejiang Lab (CN), Centre Inria de l'Université de Rennes (FR), Institute of Information Engineering (CN), Xi'an Jiaotong University (CN)
Openalex Percentile: Top 5%
Adversarial Robustness in Machine Learning
4.52
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