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ERBFT: improved asynchronous BFT with erasure code and verifiable random function

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Abstract

The asynchronous Byzantine-fault tolerance (BFT) protocols are widely adopted in mission-critical tasks. However, existing asynchronous consensus algorithms, such as Honey Badger BFT (HBBFT) and DumboBFT, still suffer from bandwidth waste, excessive producer election rounds, and low consensus efficiency. Therefore, exploring optimized solutions for current asynchronous consensus algorithms is imperative to enhance efficiency. This work proposes an improved asynchronous BFT with erasure code and verifiable random function (ERBFT). By incorporating an erasure code scheme, ERBFT optimizes the provable broadcast protocol to decrease unnecessary bandwidth wastage and message complexity. Based on the node’s performance during the broadcast phase, a comprehensive node activity evaluation mechanism has been devised to calculate the node’s activity level. When integrated with verifiable random function, this activity level facilitates the efficient selection of a producer from among the active nodes. This integration enhances the efficiency of reaching consensus and effectively diminishes the frequency of producer elections. Finally, the theoretical and experimental analysis shows that ERBFT has notably higher throughput and significantly lower latency than HBBFT, Dumbo2, and sDumbo. Specifically, when \(n=100\), compared to HBBFT, the throughput of ERBFT has increased by 137.4%, and the latency has decreased by 58.4%.

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Ethics approval and consent to participate

Not applicable

Code availability

The code used in this study is intended to support future research and is not publicly accessible. However, interested researchers may request [g2022061032@stu.mnnu.edu.cn] to obtain limited access to the code. The use of the code must comply with relevant license agreements.

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Funding

This work is supported by the National Social Science Fund of China (No.21XTQ015), the Natural Science Foundation of Fujian Province of China under Grant (No.2023J01920), the presidential research fund of Minnan Normal University (No.KJ18024), and the Fujian Provincial Education Research Project for Young and Middle-aged Teachers (No.JAT200293).

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Authors and Affiliations

  1. School of Computer Science, Minnan Normal University, Xizhi Street, Zhangzhou, 363000, Fujian, China

    Yu Lan, Hui Huang, Qunshan Chen & Shuaike Wu

  2. Key Laboratory of Data Science and Intelligence Application, Xizhi Street, Zhangzhou, 363000, Fujian, China

    Hui Huang

  3. Fujian Key Laboratory of Granular Computing and Application, Xizhi Street, Zhangzhou, 363000, Fujian, China

    Zhenjie Huang

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  1. Yu Lan
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Contributions

Yu Lan: Conceptualization, Writing-original draft, Validation Hui Huang: Methodology, Writing-review, and editing Zhenjie Huang: Supervision Qunshan Chen: Writing-review, editing Shuaike Wu: Software, Validation

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Correspondence to Hui Huang.

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Lan, Y., Huang, H., Huang, Z. et al. ERBFT: improved asynchronous BFT with erasure code and verifiable random function. J Supercomput 81, 486 (2025). https://doi.org/10.1007/s11227-025-06995-4

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