Cited by 292Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataDigital Media Forensic Detection

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PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

Zhang, Jiale, Chen, Bing, Cheng, Xiang, Binh, Huynh Thi Thanh, Yu, Shui·2021·IEEE Internet of Things Journal

DOI 10.1109/jiot.2020.3023126

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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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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)
ORCID: Zhang, Jiale
Chen, Bing
Cheng, Xiang
Binh, Huynh Thi Thanh
Yu, Shui
Venue: IEEE Internet of Things Journal
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2021-03-01
Volume: 8
Issue: 5
Pages: 3310-3322
ISSN: 2327-4662 (ISSN-L)
2372-2541
DOI: 10.1109/jiot.2020.3023126
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems

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