In this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value;however,...
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In this article, we present a finite-time stopping criterion for consensus algorithms in networks with dynamic communication topology. Prior state of the art has established convergence to the consensus value;however, the asymptotic convergence of these algorithms poses a challenge in practical settings where the response from agents is required in finite time. To this end, we propose a maximum-minimum protocol that propagates the global maximum and minimum values of agent states (while running the consensus algorithm) in the network. This article focuses on establishing that the global maximum and minimum values are strictly monotonic even for a dynamic topology, and they can be used to distributively ascertain the closeness to convergence in finite time. We rigorously show that each node can have access to the global maximum and minimum by running the proposed maximum-minimum protocol to realize a finite-time stopping criterion for the otherwise asymptotic consensus algorithm. The practical utility of the algorithm is illustrated through experiments where each agent is instantiated by a NodeJS *** server.
Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of ...
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Blockchain technology has a wide range of applications in the fields of finance, credit reporting and intellectual property, etc. As the core of blockchain, consensus algorithm affects the security and performance of blockchain system directly. In the past 10 years, there have been about 30 consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Ripple Protocol consensus algorithm (RPCA) and AlgoRand. But their security, stability and operating efficiency still lag far behind our actual needs. This paper introduces the computing power competition of PoW into DPoS to design an improved consensus algorithm named Delegated Proof of Stake with Downgrade (DDPoS). Through the further modification, the impact of both computing resources and stakes on generating blocks is reduced to achieve higher efficiency, fairness, and decentralization in consensus process. Then a downgrade mechanism is proposed to quickly replace the malicious nodes to improve the security. The simulation experiments in blockchain system show that the proposed consensus algorithm is significantly more efficient than PoW and PoS, but slightly lower than DPoS. However, its degree of centralization remains far below that of DPoS. And through the downgrade mechanism, the proposed consensus algorithm can detect and downgrade the malicious nodes timely to ensure the security and good operation of system.
This article is a study on the use of additive obfuscation signals to keep the reference values of the agents in the continuous-time Laplacian average consensus algorithm private from eavesdroppers. Obfuscation signal...
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This article is a study on the use of additive obfuscation signals to keep the reference values of the agents in the continuous-time Laplacian average consensus algorithm private from eavesdroppers. Obfuscation signals are perturbations that agents add to their local dynamics and their transmitted-out messages to conceal their private reference values. An eavesdropper is an agent inside or outside the network that has access to some subset of the interagent communication messages, and its knowledge set also includes the network topology. Rather than focusing on using a zero-sum and vanishing additive signal, our work determines the necessary and sufficient conditions that define the set of admissible obfuscation signals that do not perturb the convergence point of the algorithm from the average of the reference values of the agents. Of theoretical interest, our results show that this class includes nonvanishing signals as well. Given this broader class of admissible obfuscation signals, we define a deterministic notion of privacy preservation. In this definition, privacy preservation for an agent means that neither the private reference value nor a finite set of values to which the private reference value of the agent belongs to can be obtained. Then, we evaluate the agents' privacy against eavesdroppers with different knowledge sets.
Supported by cloud computing, Federated Learning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security...
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Supported by cloud computing, Federated Learning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security and fairness of FL implementation, numerous Blockchain-empowered Federated Learning (BFL) frameworks have emerged accordingly. Among them, consensus algorithms play a pivotal role in determining the scalability, security, and consistency of BFL systems. Existing consensus solutions to block producer selection and reward allocation either focus on well-resourced scenarios or accommodate BFL based on clients' contributions to model training. However, these approaches limit consensus efficiency and undermine reward fairness, due to involving intricate consensus processes, disregarding clients' contributions during blockchain consensus, and failing to address lazy client problems (malicious clients plagiarizing local model updates from others to reap rewards). Given the aforementioned challenges, we make the first attempt to design a joint solution for efficient consensus and fair reward allocation in heterogeneous BFL systems with lazy clients. Specifically, we introduce a generalizable BFL workflow that can address lazy client problems well. Based on it, the global contribution of BFL clients is decoupled into five dominant metrics, and the block producer selection problem is formulated as a reward-constraint contribution maximization problem. By addressing this problem, the optimal block producer that maximizes global contribution can be identified to orchestrate consensus processes, and rewards are distributed to clients in proportion to their respective global contributions. To achieve it, we develop a Context-aware Proof-of-Contribution consensus algorithm named CPoC to reach consensus and incentive simultaneously, followed by theoretical analysis of lazy client problems and privacy issues. Empirical results on widely-used datasets demonstrate the ef
The proof-of-work (PoW) algorithm has been initially used in Bitcoin and is now one of the mainstream consensus algorithms to create immutable ledgers of transactions. However, it is still not very well understood, an...
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The proof-of-work (PoW) algorithm has been initially used in Bitcoin and is now one of the mainstream consensus algorithms to create immutable ledgers of transactions. However, it is still not very well understood, and as such, its entire strength remains undervalued. While some researchers classify it as a Nakamoto consensus algorithm, some other researchers do not even consider it as a consensus algorithm. In this article, the objective is to shed light on classifying PoW. To this end, we make a theoretical analysis from a coordination perspective, and then we conclude that PoW is a stigmergic consensus algorithm where the trace left by an action in the blockchain through indirect coordination of agents stimulates a subsequent action and eventually creates a single chain of blocks. In other words, PoW is a form of stigmergic coordination toward emergent behavior of a blockchain system. Based on this identification, we further analyze the fundamental properties of PoW. Finally, we reengineer PoW by improving its missing stigmergic mechanisms, and we provide a discussion.
We propose a consensus mechanism based on the consistent hash algorithm for multiple master nodes. The consensus mechanism uses a consistent hash algorithm to select consensus group nodes and common nodes and further ...
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We propose a consensus mechanism based on the consistent hash algorithm for multiple master nodes. The consensus mechanism uses a consistent hash algorithm to select consensus group nodes and common nodes and further selects multiple master nodes on this basis, and conduct one-way communication with the final leader node.
Dear editor,Owing to the recent advancements in the field of Internet of things(Io T), the use of Io T devices has increased in every aspect of human life, such as smart cities, Internet of medical things, and Interne...
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Dear editor,Owing to the recent advancements in the field of Internet of things(Io T), the use of Io T devices has increased in every aspect of human life, such as smart cities, Internet of medical things, and Internet of vehicles [1]. Such devices gather data from the surrounding environment and communicate with each other via the Internet. However, it is difficult for such non-trustable devices to collaborate without a trusted intermediary [2]. According to IBM, blockchains [3,4],
In blockchain, the consensus algorithm is a core component that governs the trust among the participants in the blockchain activities. However, the exiting consensus algorithms suffer from performance bottle-neck such...
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In blockchain, the consensus algorithm is a core component that governs the trust among the participants in the blockchain activities. However, the exiting consensus algorithms suffer from performance bottle-neck such as low throughput, high delay, unstable performance, sustainability issues and vulnerability to targeted attacks. In this paper, we propose a new consortium blockchain consensus algorithm, referred to as Weighted Byzantine Fault Tolerance (WBFT) consensus algorithm that improves system throughput and consensus delay. We introduce a dynamic weighting mechanism for consensus nodes, which enhances the security of blockchain system by weakening the influence of malicious nodes and reduces the probability of malicious behavior. We validate the performance of WBFT experimentally and compare it against Practical Byzantine Fault Tolerance (PBFT) and Reputation-Based Byzantine Fault-Tolerance (RBFT) based approaches. The results show that WBFT substantially outperforms PBFT and RBFT in terms of system throughput, consensus delay and security.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
For distributed beamforming with multiple relay nodes (RNs), we consider a minimum mean square error (MMSE) criterion with a dual decomposition method and consensus algorithm to decide relay weights for RNs with local...
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For distributed beamforming with multiple relay nodes (RNs), we consider a minimum mean square error (MMSE) criterion with a dual decomposition method and consensus algorithm to decide relay weights for RNs with local communications between RNs. It is shown that the proposed MMSE-based distributed beamforming has a comparable performance to the maximum signal to noise ratio (MSNR) based distributed beamforming at medium to low signal to noise ratio (SNR) or with a smaller number of RNs.
Based on convex analysis, a novel consensus algorithm of dynamical points in a CAT(0) space is developed in this paper, in which the associated communication graph uniformly contains a directed spanning tree. The prop...
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Based on convex analysis, a novel consensus algorithm of dynamical points in a CAT(0) space is developed in this paper, in which the associated communication graph uniformly contains a directed spanning tree. The proposed algorithm provides an efficient method of solving consensus problems in a general CAT(0) space, while having certain robustness against weak communication. The application of the new algorithm to the distributed fusion of phylogenetic trees is shown with demonstrative-case simulations, together with a study on the algorithm's robustness and efficiency. (C) 2017 Elsevier Inc. All rights reserved.
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