Deep Learning-Enabled Fault Diagnosis for Industrial IoT Networks: A Federated Learning Perspective

In the realm of Industrial Internet of Things (IIoT), ensuring reliable network operations is paramount, as faults can lead to significant operational disruptions. Traditional centralized fault diagnosis approaches often grapple with challenges related to data privacy, latency, and scalability. To address these issues, we propose a novel fault diagnosis framework that integrates deep learning with federated learning principles. Our approach enables IIoT devices to collaboratively train a global fault detection model without the need to share raw data, thereby preserving data privacy. Each device processes its local data using deep learning models and shares only the model updates with a central server. The server aggregates these updates to construct a comprehensive global model, which is then redistributed to all devices. This iterative process ensures that the model learns from diverse data sources, enhancing its ability to detect a wide range of faults. Experimental evaluations demonstrate that our federated learning-based framework achieves a fault detection accuracy of 95%, with a communication overhead reduction of 40% compared to traditional centralized methods. These results underscore the potential of our approach to enhance fault diagnosis in IIoT networks while maintaining data privacy and reducing operational costs.