【Author】
Kim, Donghee; Doh, Inshil; Chae, Kijoon
【Source】35TH INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING (ICOIN 2021)
【Abstract】According to a recent article published by Forbes, the use of enterprise blockchain applications by companies is expanding. Private blockchain, such as enterprise blockchain, usually uses the Raft algorithm to achieve a consensus. However, the Raft algorithm can cause network split in unstable networks. When a network applying Raft split, the TPS(Transactions Per Second) is decreased, which results in decreased performance for the entire blockchain system. To reduce the probability of network split, we select a more stable node as the next leader. To select a better leader, we propose three criteria and suggest exploiting federated learning to evaluate them for network stability. As a result, we show that blockchain consensus performance is improved by lowering the probability of network split.
【Keywords】blockchain; consensus algorithm; Raft; leader election; federated learning
【标题】改进的 Raft 算法利用联邦学习增强私有区块链性能
【摘要】根据福布斯最近发表的一篇文章，公司对企业区块链应用程序的使用正在扩大。私有区块链，例如企业区块链，通常使用 Raft 算法来达成共识。但是，Raft 算法在不稳定的网络中会导致网络分裂。当一个网络应用 Raft 分裂时，TPS（Transactions Per Second）会降低，从而导致整个区块链系统的性能下降。为了减少网络分裂的概率，我们选择一个更稳定的节点作为下一个领导者。为了选择更好的领导者，我们提出了三个标准，并建议利用联邦学习来评估它们的网络稳定性。结果，我们表明通过降低网络分裂的概率来提高区块链共识性能。
【关键词】区块链；共识算法；筏;领导人选举；联邦学习
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