Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement
- 주제(키워드) blockchain , consensus algorithm , federated learning , leader election , Raft
- 지원기관 The Korean Institute of Communications and Information Sciences (KICS)
- 관리정보기술 faculty
- 등재 SCOPUS
- 발행기관 IEEE Computer Society
- 발행년도 2021
- 총서유형 Journal
- 회의명 35th International Conference on Information Networking, ICOIN 2021
- 일자 13 January 2021 through 16 January 2021
- URI http://www.dcollection.net/handler/ewha/000000175737
- ISBN 9781728191003
- 본문언어 영어
- Published As http://dx.doi.org/10.1109/ICOIN50884.2021.9333932
초록/요약
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.
more