Blockchain-based engine data trustworthy swarm learning management method

Abstract

Engine data management is of great significance for ensuring data security and sharing, as well as facilitating multi-party collaborative learning. Traditional data management approaches often involve decentralized data storage that is vulnerable to tampering, making it challenging to conduct multi-party collaborative learning under privacy protection conditions and fully leverage the value of data. Moreover, data with compromised integrity can lead to incorrect results if used for model training. Therefore, this paper aims to break down data sharing barriers and fully utilize decentralized data for multi-party collaborative learning under privacy protection conditions. We propose a trustworthy engine data management method based on blockchain technology to ensure data immutability and non-repudiation. To address the issue of limited data samples for some users resulting in poor model performance, we introduce swarm learning techniques based on centralized machine learning and design a trustworthy data management method for swarm learning, achieving trustworthy regulation of the entire process. We conduct research on engine models under swarm learning based on the NASA open dataset, effectively organizing decentralized data samples for collaborative training while ensuring data privacy and fully leveraging the value of data.