| Title: |
Robust and Secure Federated Learning With Verifiable Differential Privacy |
| Authors: |
Zhang, Chushan; Weng, Jian; Weng, Jiasi; Zhong, Yijian; Liu, Jia-Nan; Deng, Cunle |
| Contributors: |
National Natural Science Foundation of China; Science and Technology Major Project of Tibetan Autonomous Region of China; Open Research Fund of Machine Learning and Cyber Security Interdiscipline Research Engineering Center of Jiangsu Province; National Joint Engineering Research Center of Network Security Detection and Protection Technology; Guangdong Key Laboratory of Data Security and Privacy Preserving; Guangdong Hong Kong Joint Laboratory for Data Security and Privacy Protection; Engineering Research Center of Trustworthy AI, Ministry of Education; National Natural Science Foundation of China Youth Project; General Project of the Guangdong Provincial Natural Science Foundation; Special Funding Project of the 17th Batch of the China Postdoctoral Science Foundation; Guangzhou Science and Technology Plan Project; Basic and Applied Basic Research Foundation of Guangdong Province; Dongguan Social Development Technology Project |
| Source: |
IEEE Transactions on Dependable and Secure Computing ; volume 22, issue 5, page 5713-5729 ; ISSN 1545-5971 1941-0018 2160-9209 |
| Publisher Information: |
Institute of Electrical and Electronics Engineers (IEEE) |
| Publication Year: |
2025 |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| DOI: |
10.1109/tdsc.2025.3574745 |
| Availability: |
https://doi.org/10.1109/tdsc.2025.3574745; http://xplorestaging.ieee.org/ielx8/8858/11150357/11017481.pdf?arnumber=11017481 |
| Rights: |
https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html ; https://doi.org/10.15223/policy-029 ; https://doi.org/10.15223/policy-037 |
| Accession Number: |
edsbas.7C220831 |
| Database: |
BASE |