Defense against backdoor attack in federated learning
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The defender at the post-aggregation stage can handle model poisoning backdoor attacks, including model replacement backdoor attack and weight scaling backdoor attack.
“The defender at this stage can well handle the model poisoning backdoor attacks, including model replacement backdoor attack [37], [208], [223] and weight scaling backdoor attack [38], [194].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [223]
Cites
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Federated Learning for Healthcare Informatics
Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00082-4 · Cited by 1,542 · see citing works
How to backdoor federated learning
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Trojaning Attack on Neural Networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., & Zhang, X. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23291 · Cited by 1,270 · see citing works
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Data Poisoning Attacks Against Federated Learning Systems
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Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses for Federated Learning
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Support vector machines under adversarial label contamination
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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
“The defender at this stage can well handle the model poisoning backdoor attacks, including model replacement backdoor attack [37], [208], [223] and weight scaling backdoor attack [38], [194].”
BackgroundA Survey of Security Strategies in Federated Learning: Defending Models, Data, and Privacy
Manzoor, H. U., Shabbir, A., Chen, A., Flynn, D., & Zoha, A. (2024) · Future Internet · doi:10.3390/fi16100374 · Cited by 58 · see citing works
- Authors
- Lu, S., Li, R., Liu, W., & Chen, X.
- Affiliations
- Air Force Engineering University (CN)
- Guangzhou University (CN)
- Corresponding author
- Lu, Shiwei
- Venue
- Computers & Security
- Publisher
- Elsevier BV
- Published
- 2022-10
- Volume
- 121
- Pages
- 102819
- ISSN
- 0167-4048 (ISSN-L)
- OpenAlex
- W4283702393
- Cited by
- 48
- References
- 15
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 21 · 2023: 4
- FWCI
- 3.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers & Security (publishedVersion)
- Funders
- National Natural Science Foundation of China (11801564, 11901579, 2021JM-216, 2021JQ-335, 2022JQ-046, 62072128)
- 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
- backdoor attacks, federated learning, backdoor detection, similarity measurement, adversarial robustness, privacy preservation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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