ADFL: A Poisoning Attack Defense Framework for Horizontal Federated Learning
Abstract
Recently, federated learning has received widespread attention, which will promote the implementation of artificial intelligence technology in various fields. Privacy-preserving technologies are applied to users’ local models to protect users’ privacy. Such operations make the server not see the true model parameters of each user, which opens wider door for a malicious user to upload malicious parameters and make the training result converge to an ineffective model. To solve this problem, in this article, we propose a poisoning attack defense framework for horizontal federated learning systems called ADFL. Specifically, we design a proof generation method for users to generate proofs to verify whether it is malicious or not. An aggregation rule is also proposed to make sure the global model has a high accuracy. Several verification experiments were conducted and the results show that our method can detect malicious user effectively and ensure the global model has a high accuracy.
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Both model and data poisoning produce malicious updates to the model training of FL systems, and it is difficult to detect such attackers.
“Both methods will produce malicious updates to the model training of FL systems, and it is difficult to detect such attackers (Fang et al. 2020;Guo et al. 2022).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Guo et al. 2022
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- Authors
- Guo, J., Li, H., Huang, F., Liu, Z., Peng, Y., Li, X., Ma, J., Menon, V. G., & Igorevich, K. K.
- Affiliations
- Xidian University (CN)
- Jinan University (CN)
- SCMS Group of Educational Institutions (IN)
- South Ural State University National Research University, Celabinsk, Russian Federation
- ORCID
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2022-10
- Volume
- 18
- Issue
- 10
- Pages
- 6526-6536
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W4226057940
- Cited by
- 49
- References
- 26
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 14 · 2023: 15
- FWCI
- 3.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Foundation for Innovative Research Groups
- National Natural Science Foundation of China (62121001, 61602360, 62172314, 61872283, 61802146, 61962022, 61672415, 61906075, 61872088)
- National Science Foundation for Distinguished Young Scholars of China (62125205)
- Natural Science Basic Research Program of Shaanxi (2022JQ-603)
- S&T Program of Hebei (20310102D)
- China Postdoctoral Science Foundation (2018M633473)
- Basic and Applied Basic Research Foundation of Guangdong Province (2019A1515011017, 2019A1515011920)
- Fundamental Research Funds for the Central Universities (21618332)
- Key Research and Development Program of Shaanxi (2022GY-029)
- Higher Education Discipline Innovation Project (B16037)
- 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
- horizontal federated learning, poisoning attacks, malicious user detection, proof generation, aggregation rules, privacy-preserving machine learning
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