Gradient Leakage Attacks
Gradient Leakage Attacks is a type of privacy attack in federated learning where an adversary reconstructs sensitive raw data or infers private information by analyzing shared model gradients, exploiting the inherent information leakage in gradient updates during distributed training.
78
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5.8K
Citations
295
Authors
99
Institutions
Publications per year
2017–2026
78
Variants
Gradient Inversion Attacks
Parents
78
VerifyNet: Secure and Verifiable Federated Learning
Guowen Xu, Hongwei Li, Sen Liu et al. · IEEE Transactions on Information Forensics and Security · 2019 · 792 citations
A Taxonomy of Attacks on Federated Learning
Malhar Jere, Tyler Farnan, Farinaz Koushanfar · IEEE Security & Privacy · 2020 · 215 citations
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Zhejiang University
Hangzhou, China
5 h-index
9 publications
375 citations
Peng Cheng Laboratory
Shenzhen, China
5 h-index
10 publications
1K citations
University of Technology Sydney
Sydney, Australia
4 h-index
8 publications
298 citations
University of California, Berkeley
Berkeley, United States
4 h-index
8 publications
2.6K citations
Georgia Institute of Technology
Atlanta, United States
4 h-index
11 publications
616 citations
Zhejiang University of Science and Technology
Hangzhou, China
4 h-index
13 publications
722 citations
Tsinghua University
Beijing, China
4 h-index
10 publications
361 citations

Technische Universität Darmstadt
Darmstadt, Germany
3 h-index
13 publications
584 citations
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Repository · 18 publications · 1.5K citations
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6 publications · 1.3K citations
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2 publications · 88 citations
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2 publications · 93 citations
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TON · 2 publications · 84 citations
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