Multi-task learning for PBFT optimisation in permissioned blockchains

IF 5.3 2区材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

ACS Applied Nano Materials Pub Date : 2024-09-01 DOI:10.1016/j.bcra.2024.100206

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引用次数: 0

Abstract

Finance, supply chains, healthcare, and energy have an increasing demand for secure transactions and data exchange. Permissioned blockchains fulfilled this need thanks to the consensus protocol that ensures that participants agree on a common value. One of the most widely used protocols in private blockchains is the Practical Byzantine Fault Tolerance (PBFT), which tolerates up to one-third of Byzantine nodes, performs within partially synchronous systems, and has superior throughput compared to other protocols. It has, however, an important bandwidth consumption:

2 N (N - 1)

messages are exchanged in a system composed of N nodes to validate only one block.

It is possible to reduce the number of consensus participants by restricting the validation process to nodes that have demonstrated high levels of security, rapidity, and availability. In this paper, we propose the first database that traces the behavior of nodes within a system that performs PBFT consensus. It reflects their level of security, rapidity, and availability throughout the consensus. We first investigate different Single-Task Learning (STL) techniques to classify the nodes within our dataset. Then, using Multi-Task Learning (MTL) techniques, the results are much more interesting, with classification accuracies over 98%. Integrating node classification as a preliminary step to the PBFT protocol optimizes the consensus. In the best cases, it is able to reduce the latency by up to 94% and the communication traffic by up to 99%.

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允许区块链中 PBFT 优化的多任务学习

金融、供应链、医疗保健和能源行业对安全交易和数据交换的需求与日俱增。由于共识协议可确保参与者就共同的价值达成一致，许可区块链满足了这一需求。在私有区块链中使用最广泛的协议之一是实用拜占庭容错协议（PBFT），该协议可容忍多达三分之一的拜占庭节点，在部分同步系统中执行，与其他协议相比具有更高的吞吐量。不过，它也有一个重要的带宽消耗问题：在一个由 N 个节点组成的系统中，仅验证一个区块就要交换 2N(N-1)条信息。在本文中，我们提出了首个可追溯执行 PBFT 共识的系统内节点行为的数据库。它反映了整个共识过程中节点的安全性、快速性和可用性水平。我们首先研究了不同的单任务学习（STL）技术，以对数据集中的节点进行分类。然后，使用多任务学习（MTL）技术，结果更加有趣，分类准确率超过 98%。将节点分类作为 PBFT 协议的第一步，可以优化共识。在最佳情况下，它能将延迟时间减少 94%，通信流量减少 99%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Nano Materials Multiple-

CiteScore

8.30

自引率

3.40%

发文量

1601

期刊介绍： ACS Applied Nano Materials is an interdisciplinary journal publishing original research covering all aspects of engineering, chemistry, physics and biology relevant to applications of nanomaterials. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important applications of nanomaterials.