Iterative approximate byzantine consensus in arbitrary directed graphs

Vaidya, Nitin H.; Tseng, Lewis; Liang, Guanfeng

doi:10.1145/2332432.2332505

Cited by 182 publications

(290 citation statements)

References 18 publications

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“…In a recent paper, developed independently of our work, Vaidya et al have characterized tight conditions for resilient consensus using the MSR algorithm when the threat model is Byzantine and the scope is F -total [28]. The network constructions used in [28] are very similar to the robust digraphs presented here.…”

Section: A Previous Work On Consensus With Only Local Informationmentioning

confidence: 89%

“…For the case of time-invariant networks, we provide, for the first time, a tight condition for the W-MSR algorithm to succeed under the Ftotal malicious model. Furthermore, we give tight conditions for F -local and F -total Byzantine threats (the proof for the Ftotal Byzantine model is different than the proof given in [28], and is stated for the more general W-MSR algorithm and in terms of network robustness). We prove separate necessary and sufficient conditions for the W-MSR algorithm under the F -local malicious, f -fraction local malicious, and ffraction local Byzantine threat models.…”

Section: B Contributionsmentioning

confidence: 91%

“…The network constructions used in [28] are very similar to the robust digraphs presented here. In particular, the networks in [28] also require redundancy of direct information exchange between subsets of nodes in the network.…”

Section: A Previous Work On Consensus With Only Local Informationmentioning

confidence: 99%

“…F -totally bounded faults have been studied in distributed computing [5], [14], [28] and mobile robotics [21]- [23] for both stopping (or crash) failures and Byzantine failures. The F -locally bounded fault model has been studied in the context of fault-tolerant broadcasting [35], [36].…”

Section: Scope Of Threatsmentioning

confidence: 99%

“…Examples of fault or threat models include non-colluding [25], malicious [24]- [26], Byzantine [14], [21], [27], [28], or 1 The network connectivity is defined as the smaller of the two following values: (i) the size of a minimal vertex cut and (ii) n − 1, where n is the number of nodes in the network.…”

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confidence: 99%

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Resilient Asymptotic Consensus in Robust Networks

LeBlanc

Zhang

Koutsoukos

et al. 2013

IEEE J. Select. Areas Commun.

573

738

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Abstract-This paper addresses the problem of resilient innetwork consensus in the presence of misbehaving nodes. Secure and fault-tolerant consensus algorithms typically assume knowledge of nonlocal information; however, this assumption is not suitable for large-scale dynamic networks. To remedy this, we focus on local strategies that provide resilience to faults and compromised nodes. We design a consensus protocol based on local information that is resilient to worst-case security breaches, assuming the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach asymptotic consensus despite the influence of the misbehaving nodes under different threat assumptions. We show that traditional metrics such as connectivity are not adequate to characterize the behavior of such algorithms, and develop a novel graph-theoretic property referred to as network robustness. Network robustness formalizes the notion of redundancy of direct information exchange between subsets of nodes in the network, and is a fundamental property for analyzing the behavior of certain distributed algorithms that use only local information.

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Section: A Previous Work On Consensus With Only Local Informationmentioning

confidence: 89%

Section: B Contributionsmentioning

confidence: 91%

Section: A Previous Work On Consensus With Only Local Informationmentioning

confidence: 99%

Section: Scope Of Threatsmentioning

confidence: 99%

mentioning

confidence: 99%

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Resilient Asymptotic Consensus in Robust Networks

LeBlanc

Zhang

Koutsoukos

et al. 2013

IEEE J. Select. Areas Commun.

573

738

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An Overview of Cooperative and Consensus Control of Multiagent Systems

Davoodi

Gallehdari

Saboori

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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There has been a growing interest toward the development of networked unmanned autonomous systems that can operate without an extensive involvement of humans. The motivation for this focus can be traced to the emergence of applications where direct human intervention is not possible due to the environmental hazards, complexity of the tasks, or other restrictions. These networks can consist of a large number of dynamical systems (agents), such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and unmanned underwater vehicles (UUVs). These systems commonly include a number of sensors, actuators, and decisionmakers. Therefore, the network of these systems is a network of a large number of sensors and actuators or, as is known in the literature, a system of systems (SoS). In this work, we provide a brief overview of controlling these networks, their applications, and the solutions that are proposed for these problems in the literature. Specifically, this article overviews recent results and progress made on multiagent consensus by focusing on cooperative control and consensus (formation) control in the presence of communication, control implementation, and saturation constraints. In addition, we review the active areas of event‐triggered consensus control, network security and attacks on the agents, and networks and resilient consensus control strategies that are proposed to handle these challenges.

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An event‐triggered approach to quantized resilient consensus

Wang

Ishii

2019

Intl J Robust & Nonlinear

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We consider an event-triggered update scheme for the problem of multi-agent consensus in the presence of faulty and malicious agents within the network. In particular, we focus on the case where the agents take integer (or quantized) values. To keep the regular agents from being affected by the behavior of faulty agents, algorithms of the mean subsequence reduced (MSR) type are employed, where neighbors taking extreme values are ignored in the updates. Different from the real-valued case, the quantized version requires the update rule to be randomized. We characterize the error bound on the achievable level of consensus among the agents as well as the necessary structure for the network in terms of the notion of robust graphs. We verify via a numerical example the effectiveness of the proposed algorithms. Copyright c

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Iterative approximate byzantine consensus in arbitrary directed graphs

Cited by 182 publications

References 18 publications

Resilient Asymptotic Consensus in Robust Networks

Resilient Asymptotic Consensus in Robust Networks

An Overview of Cooperative and Consensus Control of Multiagent Systems

An event‐triggered approach to quantized resilient consensus

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