Efficient and persistent backdoor attack by boundary trigger set constructing against federated learning
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Malicious actors often increase the persistence of backdoor attacks in FL by slowing down the learning rate during training.
“Therefore, malicious actors often increase the persistence of backdoor attacks in FL by slowing down the learning rate during training (Nguyen et al. 2024;Gong et al. 2022;Yang et al. 2023a).”
From Survey: federated learning data security and privacy-preserving in edge-Internet of Things (2024) · cites this paper as Yang et al. 2023a
Cites
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
Data Poisoning Attacks on Federated Machine Learning
Sun, G., Cong, Y., Dong, J., Wang, Q., Lyu, L., & Liu, J. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3128646 · Cited by 310 · see citing works
Rodríguez-Barroso, N., Martínez-Cámara, E., Luzón, M. V., & Herrera, F. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108588 · Cited by 38 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Survey: federated learning data security and privacy-preserving in edge-Internet of Things
Li, H., Ge, L., & Tian, L. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10774-7 · Cited by 112 · see citing works
“Therefore, malicious actors often increase the persistence of backdoor attacks in FL by slowing down the learning rate during training (Nguyen et al. 2024; Gong et al. 2022; Yang et al. 2023a).”
- Authors
- Yang, D., Luo, S., Zhou, J., Pan, L., Yang, X., & Xing, J.
- Affiliations
- Beijing Institute of Technology (CN)
- Corresponding author
- Luo, Senlin
- Venue
- Information Sciences
- Publisher
- Elsevier BV
- Published
- 2023-12
- Volume
- 651
- Pages
- 119743
- ISSN
- 0020-0255 (ISSN-L)
- OpenAlex
- W4387233540
- Cited by
- 9
- References
- 4
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 6
- FWCI
- 0.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Information Sciences (publishedVersion)
- Funders
- National Health Commission of the People's Republic of China
- 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
- backdoor attacks, federated learning, trigger optimization, class boundaries, autoencoder, locality-sensitive hashing, non-IID data, malicious clients, poisoning attacks
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
