Privacy-preserving federated learning framework with irregular-majority users

Privacy-preserving federated learning framework with irregular-majority users

In response to the existing problems that the federated learning might lead to the reduction of aggregation efficiency by handing the majority of irregular users and the leak of parameter privacy by adopting plaintext communication, a framework of privacy-preserving robust federated learning was pro...

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Bibliographic Details
Main Authors: Qianxin CHEN, Renwan BI, Jie LIN, Biao JIN, Jinbo XIONG
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2022-02-01
Series:网络与信息安全学报
Subjects:
federated learning
privacy-preserving
secure aggregation
irregular-majority users
security division protocol
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2022011
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