Privacy-Preserving Federated Learning for Predictive Maintenance in Smart Manufacturing Networks

Abstract

Smart manufacturing environments require advanced predictive maintenance capabilities, yet implementation faces significant barriers due to data privacy concerns and proprietary knowledge protection requirements. Traditional machine learning approaches necessitate centralized data repositories, creating obstacles for collaborative maintenance optimization across organizational boundaries. This research develops and evaluates a federated learning framework that enables effective predictive maintenance while preserving data privacy in manufacturing networks. The study implemented a horizontal federated learning architecture with secure aggregation protocols and differential privacy techniques across multiple aerospace manufacturing facilities. System performance was evaluated through comparative analysis against centralized and standalone approaches across multiple predictive maintenance use cases. The federated approach achieved 93.7% of centralized model accuracy while eliminating cross-facility data sharing, with failure prediction lead times approaching centralized performance while substantially outperforming standalone models. Computational overhead increased modestly, but network data transfer requirements decreased by 94%. Privacy analysis confirmed that proprietary process parameters could not be reconstructed from shared model updates. Research advances smart manufacturing capabilities by providing practical implementation framework for privacy-preserving predictive maintenance across organizational boundaries.

Article history: Received (April 30, 2025); Revised (June 23, 2025); Accepted (July 24, 2025); Published online (August 18, 2025)