Secure and Scalable Federated Learning for Predictive Maintenance in Industry 4.0 Environments

Abstract

The developing industry 4.0 and the spread of the Industrial Internet of Things (IIoT) devices transformed the concept of predictive maintenance (PdM) into consistent tracking and decision based on data. Regardless, classical centralized machine learning-based PdM solutions have brought along essential challenges such as data privacy breach, excessive communication overhead, and insufficient heterogeneity across industrial settings. To overcome these shortcomings, in this paper a new federated learning (FL) framework is provided that allows predictive maintenance to be done in an Industry 4.0 ecosystem in a decentralized, secure, and scalable manner. Our framework enables collective training of global model in collaboration with each other, in contrast to centralized methods that require transfer of raw data, thus protecting the secrecy of operational functions and aligned with the data governance requirements. In the proposed system potentially numerous privacy-preserving mechanisms are used, such as differential privacy (DP) to obfuscate local gradients and homomorphic encryption (HE) to allow secure aggregation of encrypted model updates. Moreover, we proposed an algorithm allowing the selection of a client in an adaptive way that emphasizes high-quality clients (and stable ones) according to statistical contribution and reliability to quicken convergence and lower training variance. Though a slight trade-off to model accuracy is created by privacy enhancements, experimental evaluation on the NASA C-MAPSS turbofan engine degradation dataset shows that the proposed secure FL-based PdM framework offers near-centralized prediction accuracy with dramatic decreases of communication cost and an effective defense against data inference attacks. In this paper, the author underlines the possibility of federated learning as a premise of secure, intelligent, and scalable maintenance approaches in future industrial automations.