Data Poisoning Attacks
Data Poisoning Attack is a type of adversarial manipulation in federated learning wherein a malicious participant intentionally introduces corrupted or misleading data during the training process to compromise the integrity, accuracy, or convergence of the global model.
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Publications
23.3K
Citations
1.1K
Authors
334
Institutions
Publications per year
2017–2026
331
Variants
Data Poisoning Attack
Parents
342
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen, Chang Liu, Bo Li et al. · arXiv (Cornell University) · 2017 · 1K citations · Full text
Yiming Li, Yong Jiang, Zhifeng Li et al. · IEEE Transactions on Neural Networks and Learning Systems · 2022 · 574 citations
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334

Zhejiang University
Hangzhou, China
10 h-index
16 publications
1.2K citations
Duke University
Durham, United States
10 h-index
22 publications
1.4K citations
University of Maryland, College Park
College Park, United States
10 h-index
41 publications
3.5K citations
Tsinghua University
Beijing, China
10 h-index
24 publications
3.2K citations

University of Cagliari
Cagliari, Italy
9 h-index
20 publications
3.8K citations

Huazhong University of Science and Technology
Wuhan, China
9 h-index
18 publications
917 citations

Commonwealth Scientific and Industrial Research Organisation
Canberra, Australia
8 h-index
12 publications
691 citations

Data61
Sydney, Australia
8 h-index
12 publications
595 citations
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Repository · 73 publications · 6.3K citations
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8 publications · 913 citations
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Conference · 9 publications · 625 citations
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10 publications · 1.1K citations
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5 publications · 553 citations
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