Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement

Donghee Kim; Inshil Doh; Kijoon Chae

doi:10.1109/icoin50884.2021.9333932

Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement

Donghee Kim, Inshil Doh, Kijoon Chae

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

21 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 language	English
Title of host publication	35th International Conference on Information Networking, ICOIN 2021
Publisher	IEEE Computer Society
Pages	828-832
Number of pages	5
ISBN (Electronic)	9781728191003
DOIs	https://doi.org/10.1109/icoin50884.2021.9333932
State	Published - 13 Jan 2021
Event	35th International Conference on Information Networking, ICOIN 2021 - Jeju Island, Korea, Republic of Duration: 13 Jan 2021 → 16 Jan 2021

Publication series

Name	International Conference on Information Networking
Volume	2021-January
ISSN (Print)	1976-7684

Conference

Conference	35th International Conference on Information Networking, ICOIN 2021
Country/Territory	Korea, Republic of
City	Jeju Island
Period	13/01/21 → 16/01/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Keywords

Raft
blockchain
consensus algorithm
federated learning
leader election

Access to Document

10.1109/icoin50884.2021.9333932

Cite this

Kim, D., Doh, I., & Chae, K. (2021). Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement. In 35th International Conference on Information Networking, ICOIN 2021 (pp. 828-832). Article 9333932 (International Conference on Information Networking; Vol. 2021-January). IEEE Computer Society. https://doi.org/10.1109/icoin50884.2021.9333932

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title = "Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement",

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.",

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author = "Donghee Kim and Inshil Doh and Kijoon Chae",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 35th International Conference on Information Networking, ICOIN 2021 ; Conference date: 13-01-2021 Through 16-01-2021",

year = "2021",

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doi = "10.1109/icoin50884.2021.9333932",

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Kim, D, Doh, I & Chae, K 2021, Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement. in 35th International Conference on Information Networking, ICOIN 2021., 9333932, International Conference on Information Networking, vol. 2021-January, IEEE Computer Society, pp. 828-832, 35th International Conference on Information Networking, ICOIN 2021, Jeju Island, Korea, Republic of, 13/01/21. https://doi.org/10.1109/icoin50884.2021.9333932

Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement. / Kim, Donghee; Doh, Inshil; Chae, Kijoon.
35th International Conference on Information Networking, ICOIN 2021. IEEE Computer Society, 2021. p. 828-832 9333932 (International Conference on Information Networking; Vol. 2021-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

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KW - leader election

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M3 - Conference contribution

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Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement

Abstract

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Keywords

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