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Data Poisoning Attacks Against Federated Learning Systems

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Tolpegin, Vale, Truex, Stacey, Gursoy, Mehmet Emre, Liu, Ling·2020·Lecture Notes in Computer Science

DOI 10.1007/978-3-030-58951-6_24

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Authors: Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L.
Affiliations: Georgia Institute of Technology (US)
Corresponding author: Truex, Stacey
ORCID: Truex, Stacey
Liu, Ling
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2020
Pages: 480-501
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-030-58951-6_24
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
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
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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