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ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
Abstract
Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol. Unfortunately, PPFL is vulnerable to model poisoning attacks launched by a Byzantine adversary, who crafts malicious local gradients to harm the accuracy of the federated model. To resist model poisoning attacks, existing defense strategies focus on identifying suspicious local gradients over plaintexts. However, the Byzantine adversary submits encrypted poisonous gradients to circumvent existing defense strategies in PPFL, resulting in encrypted model poisoning. To address the issue, in this paper we design a privacy-preserving defense strategy using two-trapdoor homomorphic encryption (referred to as ShieldFL), which can resist encrypted model poisoning without compromising privacy in PPFL. Specially, we first present the secure cosine similarity method aiming to measure the distance between two encrypted gradients. Then, we propose the Byzantine-tolerance aggregation using cosine similarity, which can achieve robustness for both Independently Identically Distribution (IID) and non-IID data. Extensive evaluations on three benchmark datasets (i.e.,MNIST, KDDCup99, and Amazon) show that ShieldFL outperforms existing defense strategies. Especially, ShieldFL can achieve 30%-80% accuracy improvement to defend two state-of-the-art model poisoning attacks in both non-IID and IID settings.
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“To solve this problem, [38], [49], [51] used homomorphic encryption to evaluate the local model securely.”
From Poisoning attacks in federated learning: A survey (2023) · cites this paper as [38]
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Efficient verifiable secure aggregation protocols for federated learning
Xu, B., Wang, S., & Tian, Y. (2025) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2025.104161 · Cited by 2
Xue, L., Zhong, L., Zhang, J., Chen, Z., Cheng, H., & Li, J. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3617540 · Cited by 2
Xue, K., Li, J., Xue, R., Xue, Y., & Zhao, J. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3632902 · Cited by 3
DamPa: Dynamic Adaptive Model Poisoning Attack in Federated Learning
Hu, F., Zhang, A., Liu, X., & Li, M. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3631449 · Cited by 2
Metadata
- Authors
- Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H.
- Affiliations
- Xidian University (CN)
- University of Oregon (US)
- Singapore Management University (SG)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2022
- Volume
- 17
- Pages
- 1639-1654
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W4226047321
- Cited by
- 371
- References
- 39
- Citations by year
- 2026: 98 · 2025: 123 · 2024: 94 · 2023: 50 · 2022: 6
- FWCI
- 22.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion)
- Funders
- National Key Research and Development Program of China (2021YFB3101100)
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China (62121001)
- National Natural Science Foundation of China (62072361, U1804263, 62072352)
- Key Research and Development Program of Shaanxi Province (2019ZDLGY12-04, 2020ZDLGY09-06)
- Fundamental Research Funds for the Central Universities (JB211505)
- Guangxi Key Laboratory of Cryptography and Information Security (GCIS201917)
- Guangxi Key Laboratory of Trusted Software (KX202028)
- Ripple University Blockchain Research Initiative
- 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 Networks and Communications › Cryptography and Data Security
- Keywords
- privacy-preserving federated learning, model poisoning, Byzantine attacks, gradient aggregation, non-IID data
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