The DOI matches its Crossref record; 40 of the 44 references it cites were found in a registry or an index.
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]
Citations
Cites
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Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,889
Communication-Efficient Learning of Deep Networks from Decentralized Data
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. Y. (2016) · arXiv · doi:10.48550/arxiv.1602.05629 · Cited by 14,215
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Density-Based Clustering Based on Hierarchical Density Estimates
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Artificial intelligence and statistics
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Towards Federated Learning at Scale: System Design
Bonawitz, K., Eichner, H., Grieskamp, W., Huba, D., Ingerman, A., Ivanov, V., Kiddon, C., Konečný, J., Mazzocchi, S., McMahan, H. B., et al. (2019) · arXiv · doi:10.48550/arxiv.1902.01046 · Cited by 2,334
Nasr, M., Shokri, R., & Houmansadr, A. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00065 · Cited by 1,760
How to backdoor federated learning
Bagdasaryan, E., Veit, A., Hua, Y., Estrin, D., & Shmatikov, V. (2018) · arXiv · doi:10.48550/arxiv.1807.00459 · Cited by 1,495
Poisoning Attacks against Support Vector Machines
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Cited by
Sorted by citations
Items 101–110 of 110
FedCleanse: A Post-Hoc Unlearning Technique for Federated Learning Against Label Flipping Attacks
Li, S., Zhan, P., & Feng, Y. (2026) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1109/eibdct69742.2026.11567006
Federated Learning Poisoning Guard in Non-IID IDS
Rao, X.-M., Chang, L.-H., & Lee, T. H. (2025) · IEEE Globecom Workshops (GC Wkshps) · doi:10.1109/gcwkshps68340.2025.11591180
Kumar, A. D., & Sarath, G. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.431
Defending Federated Learning Against Model Poisoning Attacks via Eliminating Malicious Features
Qiu, W., Liu, Q., Zeng, Z., Tian, Y., Jiang, Z. L., & Liu, Y. (2026) · International Conference on Big Data and Privacy Computing (BDPC) · doi:10.1109/bdpc69980.2026.11608099
Layer- and Density-Aware Backdoor Defense for Federated LearningPreprint
Hwang, H., Choe, M., Ryu, G., Seo, C., Cho, H., & Kim, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7199288
Aggressive, Imperceptible, or Both: Architecture-Aware Hybrid Byzantines in Federated Learning
Özfatura, E., Özfatura, K., Buyukates, B., Coşkuner, M., Küpçü, A., & Gündüz, D. (2026) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp68448.2026.00052
Hamza, A., Ye, X., Lu, P.-K., Chiu, C.-Y., & Wang, C.-H. (2026) · IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) · doi:10.1109/icce-taiwan71481.2026.11652767
Self-supervised Clustering Defense Against Federated Learning Poisoning Attack in IIoT
Wang, S., Li, Q., Cui, S., Jiang, R., & Tang, Y. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31236-5_43
Unmasking Composite Adversaries: A New Threat Model for Federated Learning
Hasan, M., Xiang, Y., Uddin, M. P., Yearwood, J. L., & Gao, L. (2026) · IEEE Transactions on Reliability · doi:10.1109/tr.2026.3736807
RFLDefense: robust federated learning defense against targeted attacks
Shi, X., Gong, J., & He, Y. (2026) · Cybersecurity · doi:10.1186/s42400-026-00679-z
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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