Multiparty Secure Broad Learning System for Privacy Preserving-Reference-Cited by-同舟云学术

Multiparty Secure Broad Learning System for Privacy Preserving

Published:2023-10 Issue:10 Volume:53 Page:6636-6648
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Cao Xiao-Kai¹^ORCID,Wang Chang-Dong¹^ORCID,Lai Jian-Huang¹^ORCID,Huang Qiong²^ORCID,Chen C. L. Philip³^ORCID

Affiliation:

1. School of Computer Science and Engineering and the Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China

2. College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China

3. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

Funder

National Key Research and Development Program of China

NSFC

Science and Technology Program of Guangzhou

Science and Technology Major Project of Guangzhou

Program for Guangdong Introducing Innovative and Entrepreneurial Teams

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/10252107/10025360.pdf?arnumber=10025360

Reference44 articles.

1. Federated optimization in heterogeneous networks;li;Proc Mach Learn Syst,2020

2. CAFE: Catastrophic data leakage in vertical federated learning;jin;arXiv 2110 15122,2021

3. Protocols for secure computations

4. Inverting gradients—How easy is it to break privacy in federated learning?;geiping;Proc Annu Conf Neural Inf Process Syst,2020

5. Privacy Preservation in Distributed Subgradient Optimization Algorithms

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