Secure aggregation for semi-decentralized federated learning under heterogeneous data

Secure aggregation for semi-decentralized federated learning under heterogeneous data

Semi-decentralized federated learning has gained attention for combining the advantages of centralized and decentralized approaches, thereby enhancing system scalability and flexibility. However, in the open heterogeneous federated learning architecture, the existence of semi-honest participants and...

Full description

Saved in:
Bibliographic Details
Main Authors: HUANG Mei, WANG Lingling, ZHANG Zhengyin, LIU Yufei, SUN Yitong
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2025-06-01
Series:网络与信息安全学报
Subjects:
semi-decentralized federated learning
privacy protection
weighted aggregation
heterogeneous data
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2025029
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items