Laplacian Smoothing Stochastic ADMMs With Differential Privacy Guarantees-Reference-Cited by-同舟云学术

Laplacian Smoothing Stochastic ADMMs With Differential Privacy Guarantees

Published:2022 Issue: Volume:17 Page:1814-1826
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Liu Yuanyuan¹^ORCID,Geng Jiacheng¹^ORCID,Shang Fanhua²^ORCID,An Weixin¹^ORCID,Liu Hongying³^ORCID,Zhu Qi¹,Feng Wei²^ORCID

Affiliation:

1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi’an, China

2. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin, China

3. Peng Cheng Laboratory, Shenzhen, China

Funder

National Natural Science Foundation of China

Natural Science Basic Research Program of Shaanxi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9652463/09762729.pdf?arnumber=9762729

Reference45 articles.

1. Faster and non-ergodic O(1/K) stochastic alternating direction method of multipliers;li;Proc Adv Neural Inf Process Syst (NIPS),2017

2. Differentially Private ADMM Algorithms for Machine Learning

3. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers

4. Efficient privacy-preserving nonconvex optimization;wang;arXiv 1910 13659,2019

5. Rényi differential privacy;mironov;Proc IEEE 30th Comput Secur Found Symp (CSF),2017

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