FEDERATED LEARNING-ENABLED DIGITAL TWIN ARCHITECTURE FOR SECURE MANUFACTURING ENVIRONMENTS

Abstract

The rapid adoption of Industry 4.0 technologies has transformed modern manufacturing environments through the integration of Industrial Internet of Things (IIoT), cyber-physical systems, and Digital Twin (DT) technologies. However, the continuous exchange of operational data among connected devices and cloud platforms raises significant concerns regarding data privacy, security, and regulatory compliance. Federated Learning (FL) has emerged as a promising distributed machine learning paradigm that enables collaborative model training without exposing sensitive local data. This study proposes a Federated Learning-Enabled Digital Twin Architecture for Secure Manufacturing Environments, where multiple manufacturing units maintain local digital twins and participate in decentralized intelligence generation through federated learning mechanisms. The proposed architecture facilitates real-time monitoring, predictive maintenance, anomaly detection, and process optimization while preserving data confidentiality. By integrating digital twins with federated learning, manufacturing enterprises can achieve enhanced operational efficiency, reduced communication overhead, and improved cybersecurity resilience. The framework supports secure model aggregation, decentralized decision-making, and adaptive learning across geographically distributed production facilities. Experimental analysis demonstrates improvements in prediction accuracy, system reliability, and data privacy compared to conventional centralized architectures. The proposed approach contributes to the development of intelligent, secure, and scalable smart manufacturing ecosystems capable of addressing emerging industrial challenges while maintaining compliance with data protection requirements.