Improved Raft Algorithm exploiting Federated Learning for Private Blockchain performance enhancement
Dong-Hee Kim, Inshil Doh, Kijoon Chae
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.