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FL-Defender: Combating targeted attacks in federated learning
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“It can be seen that some existing surveys, e.g., [44], [45], [46], and [47] considered backdoor attacks and backdoor defenses as a part of robustness threat on WFL, however, the limitations of the existing backdoor attack and defense methods were not highlighted.”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [44]
In the citing paper
“The backdoor attacks conducted during the local data collection phase can be generally classified into label-flipping-based attacks [44] and trigger-based attacks [183].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [44]
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Cited by
Sorted by citations
The Impact of Adversarial Attacks on Federated Learning: A Survey
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Metadata
- Authors
- Jebreel, N. M., & Domingo-Ferrer, J.
- Affiliations
- Universitat Rovira i Virgili (ES)
- Corresponding author
- Jebreel, Najeeb Moharram
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 260
- Pages
- 110178
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W4311457580
- Cited by
- 110
- References
- 44
- Citations by year
- 2026: 24 · 2025: 34 · 2024: 36 · 2023: 16
- FWCI
- 7.07
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Knowledge-Based Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rovira & Virgili repository (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- European Commission (13039/501100011033, 501100011033, H2020, 101006879, 871042)
- Generalitat de Catalunya (13039/501100011033, 501100011033)
- Institució Catalana de Recerca i Estudis Avançats
- Horizon 2020 Framework Programme (H2020-871042, 101006879, H2020-101006879, 13039/501100011033, 871042)
- Agencia Estatal de Investigación (501100011033, PID2021-123637NB-I00, 13039, .13039/501100011033, H2020, PID2021)
- 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
- federated learning, label-flipping attacks, backdoor attacks, attack detection, model integrity
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