Enhancing privacy preservation and trustworthiness for decentralized federated learning-Reference-Cited by-同舟云学术

Enhancing privacy preservation and trustworthiness for decentralized federated learning

Published:2023-05 Issue: Volume:628 Page:449-468
ISSN:0020-0255
Container-title:Information Sciences
language:en
Short-container-title:Information Sciences

Author:

Wang Lingling^ORCID,Zhao Xueqin,Lu Zhongkai,Wang Lin,Zhang Shouxun

Funder

National Natural Science Foundation of China

Natural Science Foundation of Shandong Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Computer Science Applications,Theoretical Computer Science,Control and Systems Engineering,Software

Reference39 articles.

1. Deep learning with differential privacy;Abadi,2016

2. Privacy-preserving deep learning via additively homomorphic encryption;Aono;IEEE Trans. Inf. Forensics Secur.,2017

3. Practical secure aggregation for privacy-preserving machine learning;Bonawitz,2017

4. Multiparty computation from somewhat homomorphic encryption;Damgård,2012

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