Performance Optimization for Noise Interference Privacy Protection in Federated Learning-Reference-Cited by-同舟云学术

Performance Optimization for Noise Interference Privacy Protection in Federated Learning

Author:

Peng Zihao¹^ORCID,Li Boyuan²,Li Le²,Chen Shengbo²^ORCID,Wang Guanghui³^ORCID,Rao Hong⁴^ORCID,Shen Cong⁵^ORCID

Affiliation:

1. School of Mathematics and Computer Science, Nanchang University, Nanchang, China

2. School of Computer and Information Engineering, Henan University, Kaifeng, China

3. School of Software and the Henan International Joint Laboratory of Intelligent Network Theory and Key Technology, Henan University, Kaifeng, China

4. School of Software, Nanchang University, Nanchang, China

5. Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA

Funder

National Natural Science Foundation of China

Kaifeng Science and Technology Major Project

Ji’An Finance and Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture

Link

Reference45 articles.

1. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption;hardy;arXiv 1711 10677,2017