IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning

• URL: http://arxiv.org/abs/2508.08031v1
• Date: Mon, 11 Aug 2025 14:36:11 GMT
• Title: IPBA: Imperceptible Perturbation Backdoor Attack in Federated Self-Supervised Learning
• Authors: Jiayao Wang, Yang Song, Zhendong Zhao, Jiale Zhang, Qilin Wu, Junwu Zhu, Dongfang Zhao,
• Abstract summary: Federated self-supervised learning (FSSL) combines the advantages of decentralized modeling and unlabeled representation learning.<n>Research indicates that FSSL remains vulnerable to backdoor attacks.<n>We propose an imperceptible and effective backdoor attack method against FSSL, called IPBA.
• Score: 13.337697403537488
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated self-supervised learning (FSSL) combines the advantages of decentralized modeling and unlabeled representation learning, serving as a cutting-edge paradigm with strong potential for scalability and privacy preservation. Although FSSL has garnered increasing attention, research indicates that it remains vulnerable to backdoor attacks. Existing methods generally rely on visually obvious triggers, which makes it difficult to meet the requirements for stealth and practicality in real-world deployment. In this paper, we propose an imperceptible and effective backdoor attack method against FSSL, called IPBA. Our empirical study reveals that existing imperceptible triggers face a series of challenges in FSSL, particularly limited transferability, feature entanglement with augmented samples, and out-of-distribution properties. These issues collectively undermine the effectiveness and stealthiness of traditional backdoor attacks in FSSL. To overcome these challenges, IPBA decouples the feature distributions of backdoor and augmented samples, and introduces Sliced-Wasserstein distance to mitigate the out-of-distribution properties of backdoor samples, thereby optimizing the trigger generation process. Our experimental results on several FSSL scenarios and datasets show that IPBA significantly outperforms existing backdoor attack methods in performance and exhibits strong robustness under various defense mechanisms.

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