Analysis of OPC Data Using Federated Learning: An Evaluation of Performance and Privacy

This study examines the benefits of applying federated learning (FL) technology to OPC (Operational Performance Control) systems within industrial automation and data analysis processes. FL enables each production facility to process its data locally while only transmitting model parameters to a central server, thereby preserving data privacy. This approach provides significant advantages in industrial environments, particularly concerning data privacy and communication costs. The study evaluates FL's potential to ensure data privacy, reduce communication costs, improve efficiency in training time, and deliver high performance in predictive maintenance and quality estimation. Model performance was analyzed using accuracy, F1 score, precision, and loss metrics; the results demonstrated that FL achieved a 90% accuracy rate, offering competitive performance compared to centralized modeling. In predictive maintenance and quality analysis specifically, FL achieved 85-88% accuracy while reducing network data load by 65%. These findings validate that FL provides a secure, cost-effective, and efficient solution for industrial data analysis processes by eliminating the need for centralized data collection. In conclusion, FL and OPC integration supports data privacy, cost savings, and communication efficiency in industrial processes. The study highlights that FL could become a prevalent technology in industrial data analysis, establishing a new standard particularly in digital manufacturing processes.