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Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement
- Title
- Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement
- Authors
- Kim D.; Doh I.; Chae K.
- Ewha Authors
- 채기준; 도인실
- SCOPUS Author ID
- 채기준; 도인실
- Issue Date
- 2021
- Journal Title
- International Conference on Information Networking
- ISSN
- 1976-7684
- Citation
- International Conference on Information Networking vol. 2021-January, pp. 828 - 832
- Keywords
- blockchain; consensus algorithm; federated learning; leader election; Raft
- Publisher
- IEEE Computer Society
- Indexed
- SCOPUS
- Document Type
- Conference Paper
- 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. © 2021 IEEE.
- DOI
- 10.1109/ICOIN50884.2021.9333932
- ISBN
- 9781728191003
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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