Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach
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This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL.
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Designs of data and model poisoning backdoor attacks specifically targeting WFL have started to emerge in the literature.
“While there have been no widely reported data and model poisoning backdoor attacks specifically targeting WFL, designs of such attacks have started to emerge in the literature, e.g., [35].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [35]
By taking advantage of overheard local models, misbehaved participants can construct malicious local models to escape malicious model detection at the central server, poison the global model and subsequently the benign local models, and manipulate WFL.
“By taking advantage of the overheard local models, the misbehaved participants can construct malicious local models to escape malicious model detection at the central server, poison the global model and subsequently the benign local models, and manipulate WFL [35].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [35]
With the broadcast nature of wireless interfaces, adversaries can potentially overhear local model updates from multiple or even all clients and create malicious local models to compromise WFL.
“Moreover, with the broadcast nature of wireless interfaces, adversaries can potentially overhear local model updates from multiple or even all clients and accordingly create malicious local models to compromise WFL, e.g., using generative neural networks and machine learning technologies.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [35]
A rogue client can collect local model updates that benign clients upload, take them as input to a graph adversarial encoder (GAE) to produce a malicious local model update, which can poison WFL and stop or slow down its convergence.
“For example, in [35], [114], a new adversarial attack on WFL is studied by exploiting the broadcast nature of wireless channels, where a rogue client collects the local model updates that benign clients upload. The rogue client can take these overheard local model updates as input to a graph adversarial encoder (GAE) to produce a malicious local model update. The malicious local model update can”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [35]
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Sorted by citations
Wan, Y., Qu, Y., Ni, W., Xiang, Y., Gao, L., & Hossain, E. (2024) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2024.3361451 · Cited by 152 · see citing works
“Thus, WFL can be adopted to train tasks that highly rely on sensitive private data such as clients’ personal information, location, etc. [35], [93], [94], [95], [96], [97].”
BackgroundInfluentialSelf-citation
- Authors
- Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V.
- Affiliations
- University of Cambridge (GB)
- Centro de Investigação em Sistemas Computacionais Embebidos e de Tempo-Real (PT)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Department of Engineering, Division of Electrical Engineering, University of Cambridge, Cambridge, U.K.
- Princeton University (US)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2024
- Volume
- 19
- Pages
- 3465-3480
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4391547484
- Cited by
- 41
- References
- 56
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 9 · 2023: 1
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Real-Time and Embedded Computing Systems Research Centre (CISTER) Research Unit (UIDP/UIDB/04234/2020)
- ADANET Project (PTDC/EEICOM/3362/2021)
- IBEX Project, funded by the National Funds through FCT/MCTES (PTDC/CCI-COM/4280/2021)
- AXA Research Fund
- U.S. National Science Foundation (CNS-2128448, ECCS-2335876)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- federated learning, model poisoning, graph autoencoder, undetectable attacks, convergence analysis, adversarial training
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