Differentially Private Consensus With an Event-Triggered Mechanism

Gao, Lan; Deng, Shaojiang; Ren, Wei

doi:10.1109/tcns.2018.2795703

Cited by 53 publications

(12 citation statements)

References 38 publications

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“…and 𝜆 i > 0 which fulfill the condition of event-triggering scheme (5), as shown in Figure 1, such that 𝜓 𝓁i < 0, i = 1, 2, ⋅ ⋅ ⋅ , r, 𝓁 = 1, 2, ⋅ ⋅ ⋅ , q the following conditions hold for all:…”

Section: Resultsmentioning

confidence: 99%

“…The given system in ( 13) is stochastically stable with H ∞ performance index and given positive scalars 𝜂, 𝛿, 𝜏 M 𝛾 > 0. If there exist symmetric matrices P i > 0, Q𝓁i > 0, Ř𝓁i > 0, Ř > 0, Ž𝓁i > 0, Š𝓁 > 0, W𝓁 > 0, λi > 0, Ľ𝓁 , Ǩ𝓁 and Ň𝓁 which fulfill the condition of event-triggering scheme (5), as shown in Figure 1, such that filter gains Ǎfj , Bfj , Čfj and Ďfj , i, j = 1, 2, ⋅ ⋅ ⋅ , r, 𝓁 = 1, 2, ⋅ ⋅ ⋅ , q the following conditions hold for all:…”

Section: Aslammentioning

confidence: 99%

“…Currently, networked control systems (NCS) have attracted considerable interest in academic research as well as in industrial applications as communication technologies evolve. This is due to the structure of this kind of system, [1][2][3][4][5][6] where control loops are closed by a communication loop, a control signal can be exchanged via a secure network between all components. They offer many advantages such as facility of installation, diagnosis and maintenance, flexibility, and reduction in wiring and costs of the system, which improves the credibility, efficiency, and productivity in many manufacturing planes and distributed industrial control systems.…”

Section: Introductionmentioning

confidence: 99%

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Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Shamrooz

2021

Adaptive Control & Signal

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This article concerns the event‐triggered fuzzy filter design for Takagi‐Sugeno (T‐S) fuzzy systems subject to deception attacks under the stochastic multiple time‐varying delays. A sequence of random variables, which are mutually independent but obey the Bernoulli distribution, is introduced to account for the randomly occurring communication delays. In order to efficiently utilize limited network communication bandwidth resources, the event‐triggering scheme is adopted. A fuzzy filter with the attacked input signal is presented. Moreover, due to communication delays caused by event‐triggering schemes and transmission, the filter adopts non‐synchronous premise variables with the system. Then, by utilizing a model transformation technique, the fuzzy systems are developed. Furthermore, using the piecewise Lyapunov functional method technique, the resulting criterion provides sufficient conditions to ensure that fuzzy systems under deception attacks are stochastically stable with an H∞ performance. Accordingly, the conditions for the co‐design of the fuzzy filter and event‐triggering schemes are given. Finally, numerical simulation with the industrial process provided to verify the proposed event‐triggered design.

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Section: Resultsmentioning

confidence: 99%

Section: Aslammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Shamrooz

2021

Adaptive Control & Signal

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“…Kalman filtering mechanism to preserve the DP of the input data against adversaries is derived in this work, hence providing the tradeoff between privacy and utility. In [103], a distributed ETC framework is applied for privacy-aware multi-agent networks. In this paper, the differentially private consensus problem is studied in order to improve the privacy and communication performance of a networked multi-agent system.…”

Section: F Security and Privacymentioning

confidence: 99%

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Sedghi¹,

Ijaz²,

Noor-A-Rahim³

et al. 2020

Preprint

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Network Control Systems (NCSs) have attracted much interest over the past decade as part of a move towards more decentralised control applications and the rise of cyberphysical system applications. Many practical NCSs face the challenges of limited communication bandwidth resources, reliability and lack of knowledge of network dynamics, particularly when wireless networks are involved. Machine learning (ML) combined with event-triggered control (ETC) has the potential to ease some of these challenges. For example, ML can be used to overcome the problem of a lack of network models by learning system behaviour or adapt to dynamically changing models by continually learning model dynamics. ETC can help to conserve bandwidth resources by communicating only when needed or when resources are available. Here, we present a review of the literature on work that combines ML and ETC. The literature on supervised, semi-supervised, unsupervised and reinforcement learning based approaches such as deep reinforcement learning and statistical learning in combination with ETC is explored. Furthermore, the difference between the application of these learning algorithms on model-based and model-free systems are discussed. Following the analysis of the literature, we highlight open research questions and challenges related to ML-based ETC and propose approaches to possible solutions to these challenges.

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“…The main idea behind differential privacy is to add noise to the shared information. In the works of [20][21][22][23][24], average consensus algorithms are devised to ensure differential privacy, aiming to converge to the average, while keeping the agents' initial values private. However, the introduced noise does not ensure the exact average consensus.…”

Section: Introductionmentioning

confidence: 99%

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

Ramos¹,

Aguiar²,

Kar³

et al. 2021

Preprint

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Average consensus protocols emerge with a central role in distributed systems and decision-making such as distributed information fusion, distributed optimization, distributed estimation, and control. A key advantage of these protocols is that agents exchange and reveal their state information only to their neighbors. Yet, it can raise privacy concerns in situations where the agents' states contain sensitive information. In this paper, we propose a novel (noiseless) privacy preserving distributed algorithms for multi-agent systems to reach an average consensus. The main idea of the algorithms is that each agent runs a (small) network with a crafted structure and dynamics to form a network of networks (i.e., the connection between the newly created networks and their interconnections respecting the initial network connections). Together with a re-weighting of the dynamic parameters dictating the inter-agent dynamics and the initial states, we show that it is possible to ensure that the value of each node converges to the consensus value of the original network. Furthermore, we show that, under mild assumptions, it is possible to craft the dynamics such that the design can be achieved in a distributed fashion. Finally, we illustrate the proposed algorithm with examples.

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Differentially Private Consensus With an Event-Triggered Mechanism

Cited by 53 publications

References 38 publications

Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Retracted: State estimation of Takagi‐Sugeno fuzzy system in networked control system with stochastic time delays under unknown attacks

Machine Learning in Event-Triggered Control: Recent Advances and Open Issues

Distributed design of deterministic discrete-time privacy preserving average consensus for multi-agent systems through network augmentation

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