Enhancing Byzantine robustness of federated learning via tripartite adaptive authentication
Abstract Federated learning (FL) is a distributed learning paradigm that enables model training while protecting user privacy. However, frequent communication between the server and clients also provides opportunities for attackers to intercept or tamper with parameters, thereby affecting the global...
Saved in:
| Main Authors: | Xiaomeng Li, Yanjun Li, Hui Wan, Cong Wang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-05-01
|
| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01165-y |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Secure federated learning scheme based on adaptive Byzantine defense
by: ZHOU Yousheng, et al.
Published: (2024-08-01) -
Secure federated learning scheme based on adaptive Byzantine defense
by: ZHOU Yousheng, et al.
Published: (2024-08-01) -
Leveraging Gradient Noise for Detection and Filtering of Byzantine Clients
by: Latifa Errami, et al.
Published: (2025-01-01) -
TEXTBOOKS AND TEMPLATES IN BYZANTINE STUDIES
by: Matthew Gray Marsh
Published: (2018-12-01) -
Packet-loss robust scalable authentication algorithm for compressed image streaming
by: Xiao-wei YI, et al.
Published: (2014-04-01)