Digital Twins for Critical Infrastructure Management using Federated Learning and Edge Computing

Abstract

The creation and maintenance of digital twins for critical infrastructure systems, such as power grids, water distribution networks, and transportation systems, presents significant computational challenges. Traditional centralized approaches struggle to handle the massive amounts of data generated by distributed sensors and actuators, leading to communication bottlenecks and potential privacy concerns. This paper proposes a novel framework that integrates federated learning and edge computing to overcome these limitations. The framework enables decentralized model training and updates for the digital twin, reducing communication overhead and preserving data privacy. The core mechanism involves data aggregation and processing at the edge, followed by federated learning to iteratively refine the digital twin model. This approach offers a scalable and efficient solution for infrastructure management, enabling real-time monitoring, predictive maintenance, and optimized operational decisions. The key contributions of this work lie in the synergistic combination of these technologies, delivering a robust and adaptable system for managing complex infrastructure.