Survey on incentive-driven federated learning: privacy and security

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...

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Bibliographic Details
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:
federated learning
incentive mechanisms
privacy preservation
differential privacy
game theory
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025035
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http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025035

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