The DOI matches its Crossref record; 40 of the 42 references it cites were found in a registry or an index.
PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems
Abstract
Edge computing is a key-enabling technology that meets continuously increasing requirements for the intelligent Internet-of-Things (IoT) applications. To cope with the increasing privacy leakages of machine learning while benefiting from unbalanced data distributions, federated learning has been wildly adopted as a novel intelligent edge computing framework with a localized training mechanism. However, recent studies found that the federated learning framework exhibits inherent vulnerabilities on active attacks, and poisoning attack is one of the most powerful and secluded attacks where the functionalities of the global model could be damaged through attacker's well-crafted local updates. In this article, we give a comprehensive exploration of the poisoning attack mechanisms in the context of federated learning. We first present a poison data generation method, named Data_Gen, based on the generative adversarial networks (GANs). This method mainly relies upon the iteratively updated global model parameters to regenerate samples of interested victims. Second, we further propose a novel generative poisoning attack model, named PoisonGAN, against the federated learning framework. This model utilizes the designed Data_Gen method to efficiently reduce the attack assumptions and make attacks feasible in practice. We finally evaluate our data generation and attack models by implementing two types of typical poisoning attack strategies, label flipping and backdoor, on a federated learning prototype. The experimental results demonstrate that these two attack models are effective in federated learning.
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In the citing paper
“In local model training phase backdoor attacks, an attacker is required to fully control the compromised clients, including the training data set and training algorithm [29], [32], [116], [122]-[124].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [29]
In the citing paper
“Thus, the increment of the compromised clients controlled by the attacker can contribute to improving the attack performance [29].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [29]
In the citing paper
“To solve this problem, Jiale et al. [26] use a generative adversarial network to generate samples similar to those of other clients and then use these fake samples to execute an attack.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [26]
In the citing paper
“For example, an attacker can use GAN to reconstruct other clients' datasets so that the attacker can execute the attack by generating fake data, even if the attacker lacks a specific class of sample data [26].”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [26]
Citations
Cites
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GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
Federated Machine Learning: Concept and Applications
Yang, Q., Liu, Y., Chen, T., & Tong, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3298981 · Cited by 6,322
Practical Secure Aggregation for Privacy-Preserving Machine Learning
Bonawitz, K., Ivanov, V., Kreuter, B., Marcedone, A., McMahan, H. B., Patel, S., Ramage, D., Segal, A., & Seth, K. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3133982 · Cited by 3,956
Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793
Privacy-preserving deep learning
Shokri, R., & Shmatikov, V. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813687 · Cited by 2,356
Federated Learning with Non-IID Data
Zhao, Y., Li, M., Lai, L., Suda, N., Civin, D., & Chandra, V. (2018) · arXiv · doi:10.48550/arxiv.1806.00582 · Cited by 2,077
Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734
Cited by
Sorted by citations
Heterogeneous Federated Learning: State-of-the-art and Research Challenges
Ye, M., Fang, X., Du, B., Yuen, P. C., & Tao, D. (2023) · ACM Computing Surveys · doi:10.1145/3625558 · Cited by 709
Distributed Artificial Intelligence Empowered by End-Edge-Cloud Computing: A Survey
Duan, S., Wang, D., Ren, J., Lyu, F., Zhang, Y., Wu, H., & Shen, X. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3218527 · Cited by 439
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
Issa, W., Moustafa, N., Turnbull, B., Sohrabi, N., & Tari, Z. (2022) · ACM Computing Surveys · doi:10.1145/3560816 · Cited by 347
Federated Learning for Cybersecurity: Concepts, Challenges, and Future Directions
Alazab, M., Ramu, S. P., Parimala, M., Maddikunta, P. K. R., Gadekallu, T. R., & Pham, Q. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3119038 · Cited by 326
Rodríguez-Barroso, N., Jiménez-López, D., Luzón, M. V., Herrera, F., & Martínez-Cámara, E. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.09.011 · Cited by 296
Metadata
- Authors
- Zhang, J., Chen, B., Cheng, X., Binh, H. T. T., & Yu, S.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
- University of Technology Sydney (AU)
- Venue
- IEEE Internet of Things Journal
- Published
- 2021-03-01
- Volume
- 8
- Issue
- 5
- Pages
- 3310-3322
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W3084847664
- Cited by
- 291
- References
- 42
- Citations by year
- 2026: 34 · 2025: 61 · 2024: 72 · 2023: 63 · 2022: 43 · 2021: 18
- FWCI
- 12.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- UTS ePRESS (University of Technology Sydney) (submittedVersion)
- Funders
- National Key Research and Development Program of China (2019YFB2102000)
- National Natural Science Foundation of China (61672283)
- Science and Technology on Avionics Integration Laboratory and the National Aeronautical Science Foundation of China (20175552039)
- Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18_0308)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- federated learning, generative adversarial networks, data poisoning, edge computing, Internet of Things, label flipping, backdoor attacks, privacy leakage
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