Swarm Learning for Secure and Effective Industrial Federated Big Data Analytics

Abstract

Industrial intelligent systems (IIS) play a huge role in modern industry, and their intelligent models of IIS enable diagnosis of faults, key performance indicator (KPI) prediction, and other important industrial process analysis in a data-driven way. However, the performance of intelligent models is limited by the quantity and quality of local data in specific factories. At the same time, the privacy information and security concerns contained by industrial data lead to the problem of information silos in industry. This hinders data sharing and cross-factory collaborations. To address these issues, this article makes the following contributions. First, for the first time, we empower industrial federated big data analytics (IFBDA) of IIS with swarm learning, and propose a hyperledger fabric-based IFBDA blockchain (IFBDAchain) for multifactory information sharing and collaborative modeling. Second, in the IFBDAchain, we further consider potential dishonest behaviors among federated members, and design verification and privacy protection mechanisms to ensure trustworthiness of analytics. Third, we validate the IFBDAchain with two real industrial cases. The results demonstrate the effectiveness of the IFBDAchain in fault classification and KPI prediction tasks in industry. Compared to the average values of local learning, our method increases the classification accuracy by 27.6%, 69.4%, and 33.1% under Independent and identically distributed (IID), non-IID, and unbalanced conditions, respectively. Furthermore, the root-mean-square error of the KPI prediction decreases by 33.3%, 49%, and 45.7% for the IID, non-IID, and unbalanced conditions, respectively, indicating its significant potential as a generic backbone for industrial federated Big Data analytics.