A Survey of Distributed Consensus Protocols for Blockchain Networks
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Abstract (arXiv)
Since the inception of Bitcoin, cryptocurrencies and the underlying blockchain technology have attracted an increasing interest from both academia and industry. Among various core components, consensus protocol is the defining technology behind the security and performance of blockchain. From incremental modifications of Nakamoto consensus protocol to innovative alternative consensus mechanisms, many consensus protocols have been proposed to improve the performance of the blockchain network itself or to accommodate other specific application needs. In this survey, we present a comprehensive review and analysis on the state-of-the-art blockchain consensus protocols. To facilitate the discussion of our analysis, we first introduce the key definitions and relevant results in the classic theory of fault tolerance which help to lay the foundation for further discussion. We identify five core components of a blockchain consensus protocol, namely, block proposal, block validation, information propagation, block finalization, and incentive mechanism. A wide spectrum of blockchain consensus protocols are then carefully reviewed accompanied by algorithmic abstractions and vulnerability analyses. The surveyed consensus protocols are analyzed using the five-component framework and compared with respect to different performance metrics. These analyses and comparisons provide us new insights in the fundamental differences of various proposals in terms of their suitable application scenarios, key assumptions, expected fault tolerance, scalability, drawbacks and trade-offs. We believe this survey will provide blockchain developers and researchers a comprehensive view on the state-of-the-art consensus protocols and facilitate the process of designing future protocols.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2019.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 4; 2025: 4; 2026: 1.
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- 2024Modelling crypto markets by multi-agent reinforcement learning16 Feb 2024
- 2024Preference Poisoning Attacks on Reward Model Learning2 Feb 2024
- 2024Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study28 Jan 2024
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- 2023Conceptual Framework for Autonomous Cognitive Entities3 Oct 2023
- 2023Distributed Linear Solvers and Data Heterogeneity20 Apr 2023
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4 more
- 2022Decentralized Collaborative Learning with Probabilistic Data Protection23 Aug 2022
- 2022Trustworthy Federated Learning via Blockchain13 Aug 2022
- 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System11 Nov 2021
- 2021Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment9 Apr 2021
- 2024Preference Poisoning Attacks on Reward Model Learningcited by 14
- 2024Preference Poisoning Attacks on Reward Model Learning2 Feb 2024
- 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning Systemcited by 16
- 2022Trustworthy Federated Learning via Blockchaincited by 15
- 2023Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challengescited by 3
- 2025Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inferencecited by 3
- 2021Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessmentcited by 1
- 2022Decentralized Collaborative Learning with Probabilistic Data Protectioncited by 1
- 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Taskscited by 1
- 2023Distributed Linear Solvers and Data Heterogeneitycited by 0
- 2024PPBFL: A Privacy Protected Blockchain-based Federated Learning Modelcited by 0
- 2024Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Studycited by 0
- 2024Modelling crypto markets by multi-agent reinforcement learningcited by 0
- 2025FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learningcited by 0
12 most cited of 13 All 13 (JSON)
- 2026MOSAIC-FL, a micro-service based privacy-preserving framework with application to genomics27 Jul 2026
- 2025Design and Evaluation of Cost-Aware PoQ for Decentralized LLM Inference18 Dec 2025
- 2025Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks7 Oct 2025
- 2025FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning18 Jul 2025
- 2024Modelling crypto markets by multi-agent reinforcement learning16 Feb 2024
- 2024Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study28 Jan 2024
- 2024PPBFL: A Privacy Protected Blockchain-based Federated Learning Model2 Jan 2024
- 2023Distributed Linear Solvers and Data Heterogeneity20 Apr 2023
- 2023Blockchain-Empowered Trustworthy Data Sharing: Fundamentals, Applications, and Challenges12 Mar 2023
- 2022Decentralized Collaborative Learning with Probabilistic Data Protection23 Aug 2022
- 2022Trustworthy Federated Learning via Blockchain13 Aug 2022
- 2021Fairness, Integrity, and Privacy in a Scalable Blockchain-based Federated Learning System11 Nov 2021
12 newest of 13 All 13 (JSON)