Data Poisoning Attack by Label Flipping on SplitFed Learning
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Existing works show reasonable defense performance on label-flipping backdoor attack and trigger-based backdoor attack.
“The existing works show reasonable defense performance on label-flipping backdoor attack [186], [187] and trigger-based backdoor attack [188], [189].”
From Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey (2024) · cites this paper as [187]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Trojaning Attack on Neural Networks
Liu, Y., Ma, S., Aafer, Y., Lee, W.-C., Zhai, J., Wang, W., & Zhang, X. (2018) · Proceedings 2018 Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23291 · Cited by 1,270 · see citing works
Data Poisoning Attacks Against Federated Learning Systems
Tolpegin, V., Truex, S., Gursoy, M. E., & Liu, L. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58951-6_24 · Cited by 927 · see citing works
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural NetworksPreprint
Shafahi, A., Huang, W., Najibi, M., Suciu, O., Studer, C., Dumitraş, T., & Goldstein, T. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000460348 · Cited by 626 · see citing works
Vulnerabilities in federated learning
Bouacida, N., & Mohapatra, P. (2021) · IEEE Access · doi:10.1109/access.2021.3075203 · Cited by 248 · see citing works
Support vector machines under adversarial label contamination
Huang, X. X., Biggio, B., Nelson, B. A., Xiao, H., Eckert, C. M., & Roli, F. (2015) · Neurocomputing · doi:10.1016/j.neucom.2014.08.081 · Cited by 220 · see citing works
Li, D., Wong, W. E., Wang, W., Yao, Y., & Chau, M. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00081 · Cited by 115 · see citing works
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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 · see citing works
“Considering the fact that the aggregator, which simply combines the received updates, is vulnerable to being poisoned, defenders design a robust aggregator based on statistical methods [194], [208].”
MethodSplit Federated Learning for 6G Enabled-Networks: Requirements, Challenges, and Future Directions
Hafi, H., Brik, B., Frangoudis, P. A., Ksentini, A., & Bagaa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3351600 · Cited by 90 · see citing works
- Authors
- Gajbhiye, S., Singh, P., & Gupta, S.
- Affiliations
- Indian Institute of Technology Jammu (IN)
- Dhirubhai Ambani University (IN)
- Corresponding author
- Gajbhiye, Saurabh
- Venue
- Communications in Computer and Information Science
- Publisher
- Springer Nature Switzerland
- Published
- 2023
- Pages
- 391-405
- ISSN
- 1865-0929 (ISSN-L)
- 1865-0937
- OpenAlex
- W4315491680
- Cited by
- 13
- References
- 8
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 5 · 2023: 2
- FWCI
- 2.31
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Communications in computer and information science (publishedVersion)
- 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 › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- data poisoning, label flipping, split learning, federated learning, model splitting, targeted attacks, untargeted attacks, robustness, malicious participants
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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