| Title: |
Publicly Auditable Federated Learning With Privacy and Byzantine Robustness |
| Authors: |
Zeng, Huang; Yang, Anjia; Weng, Jian; Chen, Min-Rong; Xiao, Fengjun; Liu, Zilin; Liu, Yi |
| Contributors: |
National Key Research and Development Program of China; National Natural Science Foundation of China; Science and Technology Major Project of Tibetan Autonomous Region of China; Guangdong Special Support Program; Guangzhou Basic Research Plan City-School Joint Funding Project; Guangzhou Basic and Applied Basic Research Foundation; 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; Guangdong Hong Kong Joint Laboratory for Data Security and Privacy Protection; Engineering Research Center of Trustworthy AI, Ministry of Education |
| Source: |
IEEE Transactions on Dependable and Secure Computing ; volume 23, issue 1, page 577-589 ; ISSN 1545-5971 1941-0018 2160-9209 |
| Publisher Information: |
Institute of Electrical and Electronics Engineers (IEEE) |
| Publication Year: |
2026 |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| DOI: |
10.1109/tdsc.2025.3609353 |
| Availability: |
https://doi.org/10.1109/tdsc.2025.3609353; http://xplorestaging.ieee.org/ielx8/8858/11354469/11162649.pdf?arnumber=11162649 |
| 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.4D8D42FA |
| Database: |
BASE |