Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement

Donghee Kim, Inshil Doh, Kijoon Chae

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

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.

Original languageEnglish
Title of host publication35th International Conference on Information Networking, ICOIN 2021
PublisherIEEE Computer Society
Pages828-832
Number of pages5
ISBN (Electronic)9781728191003
DOIs
StatePublished - 13 Jan 2021
Event35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of
Duration: 13 Jan 202116 Jan 2021

Publication series

NameInternational Conference on Information Networking
Volume2021-January
ISSN (Print)1976-7684

Conference

Conference35th International Conference on Information Networking, ICOIN 2021
Country/TerritoryKorea, Republic of
CityJeju Island
Period13/01/2116/01/21

Keywords

  • Raft
  • blockchain
  • consensus algorithm
  • federated learning
  • leader election

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