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Cited by 130Privacy-Preserving Technologies in DataAdversarial Robustness in Machine LearningDigital Media Forensic Detection

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

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Nguyen, Thuy Dung, Nguyen, Tuan, Nguyen, Phi Le, Pham, Hieu H., Doan, Khoa D., Wong, Kok-Seng·2024·Engineering Applications of Artificial Intelligence
DOI 10.1016/j.engappai.2023.107166
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  • 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 Nguyen et al. 2024

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References 134 · Cited by 130

Authors
Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S.
Affiliations
  • VinUniversity (VN)
  • Trường Đại học Khoa học và Công nghệ Hà Nội (VN)
Corresponding author
Wong, Kok-Seng
ORCID
Venue
Engineering Applications of Artificial Intelligence
Publisher
Elsevier BV
Published
2024-01
Volume
127
Pages
107166
ISSN
0952-1976 (ISSN-L)
DOI
10.1016/j.engappai.2023.107166
OpenAlex
W4387359750
Cited by
130
References
134
Citations by year
2026: 41 · 2025: 51 · 2024: 36 · 2023: 2
FWCI
11.51
Type
journal article
Language
en
Open access
Licence
Locations
  • Engineering Applications of Artificial Intelligence (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
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 › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
Keywords
backdoor attacks, federated learning, backdoor defense, data poisoning, malicious clients, model heterogeneity, privacy-preserving machine learning

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