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Data Poisoning Attacks Against Federated Learning Systems
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“Even with a low proportion of malicious edge servers, data poisoning attacks can significantly decrease recall and classification accuracy, as discussed by Tolpegin et al. [139].”
From Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis (2021) · cites this paper as [139]
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ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning
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Metadata
- Authors
- Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Truex, Stacey
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 480-501
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3087391814
- Cited by
- 927
- References
- 52
- Citations by year
- 2026: 124 · 2025: 219 · 2024: 243 · 2023: 160 · 2022: 113 · 2021: 62 · 2020: 6
- FWCI
- 32.57
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1564097)
- Directorate for Computer and Information Science and Engineering (1564097)
- 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
- data poisoning, federated learning, targeted attacks, malicious participants, mislabeled data, model updating
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