Event-Triggered Differentially Private Average Consensus for Multi-agent Network

Aijuan Wang; Xiaofeng Liao; Haibo He

doi:10.1109/JAS.2019.1911327

Volume 6 Issue 1

Jan. 2019

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2019 > 6(1): 75-83

Aijuan Wang, Xiaofeng Liao and Haibo He, "Event-Triggered Differentially Private Average Consensus for Multi-agent Network," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 75-83, Jan. 2019. doi: 10.1109/JAS.2019.1911327

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Aijuan Wang, Xiaofeng Liao and Haibo He, "Event-Triggered Differentially Private Average Consensus for Multi-agent Network," IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 75-83, Jan. 2019. doi: 10.1109/JAS.2019.1911327

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Event-Triggered Differentially Private Average Consensus for Multi-agent Network

doi: 10.1109/JAS.2019.1911327

Aijuan Wang^{1, 2, 3
,},
Xiaofeng Liao^{1
,
,},
Haibo He^3
,

1.
College of Computer Science, Chongqing University, Chongqing 400044
2.
School of Electronics and Information Engineering, Southwest University, Chongqing 400715, China
3.
Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island Kingston, RI 02881 USA
4.
Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, China

Funds:

the National Key Research and Development Program of China 2016YFB0800601

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Author Bio:
Aijuan Wang received the B.S. degree from Qufu Normal University, ShanDong, China, in 2014. Currently, she is working toward the Ph.D. degree in the College of Electronics and Information Engineering at Southwest University, China.
Her research interests include observer design, nonlinear dynamical systems, optimal control, eventtriggered control, neural networks, differentially private, privacy-preserving average consensus and consensus of multi-agent systems. (e-mail: aijuan321@foxmail.com)

Xiaofeng Liao received the B.S.and M.S.degrees in mathematics from Sichuan University, Chengdu, China, in 1986 and 1992, respectively, and the Ph.D. degree in circuits and systems from the University of Electronic Science and Technology of China in 1997.From 1999 to 2001, he was involved in postdoctoral research at Chongqing University, where he is currently a professor.From November 1997 to April 1998, he was a research associate at the Chinese University of Hong Kong.From October 1999 to October 2000, he was a research associate at the City University of Hong Kong.From March 2001 to June 2001 and March 2002 to June 2002, he was a senior research associate at the City University of Hong Kong, China.From March 2006 to April 2007, he was a research fellow at the City University of Hong Kong.He has published more than 200 international journal and conference papers.Currently, he is an Associate Editor of the IEEE Transactions on cybernetics.
His current research interests include neural networks, nonlinear dynamical systems, bifurcation and chaos, and cryptography. (e-mail: aijuan321@foxmail.com)

Haibo He (SM'11-F'18) received the B.S. and M.S. degrees in electrical engineering from Huazhong University of Science and Technology, China, in 1999 and 2002, respectively, and the Ph.D. degree in electrical engineering from Ohio University in 2006. Currently, he is the Robert Haas Endowed Chair Professor at the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island, USA. He has published 1 sole-author research book (Wiley), edited 1 book (Wiley-IEEE) and 6 conference proceedings (Springer), and authored and co-authored over 280 peer-reviewed journal and conference papers. He was a recipient of the IEEE International Conference on Communications Best Paper Award (2014), IEEE Computational Intelligence Society (CIS) Outstanding Early Career Award (2014), National Science Foundation (NSF) CAREER Award (2011), and Providence Business News (PBN) "Rising Star Innovator Award (2011). Currently, he is the Editor-inChief of the IEEE Transactions on Neural Networks and Learning Systems.
His research interests include adaptive learning and control, computational intelligence, machine learning and data mining, and various applications. (e-mail: haibohe@uri.edu)
Corresponding author: Xiaofeng Liao, e-mail: xfliao@cqu.edu.cn
Received Date: 2018-07-12
Revised Date: 2018-09-11
Accepted Date: 2018-10-07

Abstract

Abstract

This paper investigates the differentially private problem of the average consensus for a class of discrete-time multi-agent network systems (MANSs). Based on the MANSs, a new distributed differentially private consensus algorithm (DPCA) is developed. To avoid continuous communication between neighboring agents, a kind of intermittent communication strategy depending on an event-triggered function is established in our DPCA. Based on our algorithm, we carry out the detailed analysis including its convergence, its accuracy, its privacy and the trade-off between the accuracy and the privacy level, respectively. It is found that our algorithm preserves the privacy of initial states of all agents in the whole process of consensus computation. The trade-off motivates us to find the best achievable accuracy of our algorithm under the free parameters and the fixed privacy level. Finally, numerical experiment results testify the validity of our theoretical analysis.
- Average consensus,
- differentially private,
- eventtriggered communication,
- multi-agent network systems (MANSs)

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References(47)

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Event-Triggered Differentially Private Average Consensus for Multi-agent Network

doi: 10.1109/JAS.2019.1911327

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