Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey-Reference-Cited by-同舟云学术

Efficiency Optimization Techniques in Privacy-Preserving Federated Learning With Homomorphic Encryption: A Brief Survey

Published:2024-07-15 Issue:14 Volume:11 Page:24569-24580
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Xie Qipeng¹^ORCID,Jiang Siyang²,Jiang Linshan³^ORCID,Huang Yongzhi¹^ORCID,Zhao Zhihe⁴,Khan Salabat⁵^ORCID,Dai Wangchen²^ORCID,Liu Zhe²,Wu Kaishun¹^ORCID

Affiliation:

1. Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

2. Zhejiang Laboratory, Hangzhou, China

3. Institute of Data Science, National University Singapore, 3 Research Link, Singapore

4. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, SAR, China

5. School of Computer and Information Engineering, Qilu Institute of Technology, Jinan, China

Funder

China, NSFC

Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things

Project of DEGP

Zhejiang Lab Open Research Project

Foundation for Distinguished Young Talents in Higher Education of Guangdong

Fund which aims to improve scientific research capability of key construction disciplines in Guangdong province “Light-Weight Federal Learning Paradigm and Its Application”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6488907/10589449/10589503.pdf?arnumber=10589503

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