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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In the citing paper
“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
Deep Residual Learning for Image Recognition
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Learning Multiple Layers of Features from Tiny Images
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
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-37456-2_14 · Cited by 2,680
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
Biggio, B., Nelson, B., & Laskov, P. (2012) · arXiv · doi:10.48550/arxiv.1206.6389 · Cited by 1,331
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- C. Fung, C.J. Yoon, I. Beschastnikh, The Limitations of Federated Learning in Sybil Settings, in: 23rd International Symposium on Research in Attacks, Intrusions and Defenses, RAID 2020, 2020, pp. 301–316.
- C. Xie, K. Huang, P.-Y. Chen, B. Li, Dba: Distributed backdoor attacks against federated learning, in: International Conference on Learning Representations, 2019.
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- S. Shen, S. Tople, P. Saxena, Auror: Defending against poisoning attacks in collaborative deep learning systems, in: Proceedings of the 32nd Annual Conference on Computer Security Applications, 2016, pp. 508–519. · DOI · Paper page
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Cited by
Sorted by citations
The Impact of Adversarial Attacks on Federated Learning: A Survey
Kumar, K. N., Mohan, C. K., & Cenkeramaddi, L. R. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3322785 · Cited by 209
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
A Survey on Federated Unlearning: Challenges, Methods, and Future Directions
Liu, Z., Jiang, Y., Shen, J., Peng, M., Lam, K., Yuan, X., & Liu, X. (2024) · ACM Computing Surveys · doi:10.1145/3679014 · Cited by 182
Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions
Yaacoub, J.-P. A., Noura, H., & Salman, O. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.001 · Cited by 106
Personalized federated learning-based intrusion detection system: Poisoning attack and defense
Thein, T. T., Shiraishi, Y., & Morii, M. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.10.005 · Cited by 104
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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