ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Ma, Zhuoran; Ma, Jianfeng; Miao, Yinbin; Li, Yingjiu; Deng, Robert H.

doi:10.1109/tifs.2022.3169918

Cited by 371Privacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningCryptography and Data Security

ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Ma, Zhuoran, Ma, Jianfeng, Miao, Yinbin, Li, Yingjiu, Deng, Robert H.·2022·IEEE Transactions on Information Forensics and Security

DOI 10.1109/tifs.2022.3169918

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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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Homomorphic encryption can be used to evaluate the local model securely.
“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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Authors: Ma, Z., Ma, J., Miao, Y., Li, Y., & Deng, R. H.
Affiliations: Xidian University (CN)
University of Oregon (US)
Singapore Management University (SG)
ORCID: Ma, Zhuoran
Ma, Jianfeng
Miao, Yinbin
Li, Yingjiu
Deng, Robert H.
Venue: IEEE Transactions on Information Forensics and Security
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2022
Volume: 17
Pages: 1639-1654
ISSN: 1556-6013 (ISSN-L)
1556-6021
DOI: 10.1109/tifs.2022.3169918
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: 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 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 › Artificial Intelligence › Cryptography and Data Security
Keywords: privacy-preserving federated learning, model poisoning, Byzantine attacks, gradient aggregation, non-IID data

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ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

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