Survey on incentive-driven federated learning: privacy and security
Federated learning was enabled to allow multiple data holders to jointly complete machine learning tasks without disclosing local data. Incentivizing participants to engage in federated learning and contribute high-quality data was identified as one of the key factors for its success. However, feder...
Saved in:
Main Authors: | CHI Huanhuan, XIONG Ping, LIU Hengzhu, MA Xiao, ZHU Tianqing |
---|---|
Format: | Article |
Language: | English |
Published: |
POSTS&TELECOM PRESS Co., LTD
2025-06-01
|
Series: | 网络与信息安全学报 |
Subjects: | |
Online Access: | http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025035 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Federated Learning for Privacy-Preserving Employee Performance Analytics
by: Jay Barach
Published: (2025-01-01) -
Privacy and Security Challenges in Federated Learning for UAV Systems: A Systematic Review
by: Ahmed Saleh Sulaiman Al Farsi, et al.
Published: (2025-01-01) -
A Comprehensive Survey of Security and Privacy in UAV Systems
by: Bryce Cordill, et al.
Published: (2025-01-01) -
Enhancing Privacy in Lightweight Data Encoding for Sensitive Applications
by: Yan Wang, et al.
Published: (2025-01-01) -
Federated Learning for Cybersecurity: A Privacy-Preserving Approach
by: Edi Marian Timofte, et al.
Published: (2025-06-01)