Toward Federated Cognitive Digital Twins over the Edge-to-Cloud Continuum

Abstract

Digital Twins (DTs) are increasingly adopted to monitor, analyze, and optimize Cyber-Physical Systems (CPSs) through continuous interaction between physical assets and their digital counterparts. However, current DT architectures often rely on centralized and monolithic designs, leading to scalability, latency, and resilience issues in distributed environment such as smart cities. Moreover, they provide limited support for semantic integration and high-level reasoning, reducing the effectiveness of DT-based decision-making. Recent studies on Federated Digital Twins (FDTs) have addressed scalability by decomposing complex systems into interacting twins, but they still largely centralize intelligence in cloud components. In parallel, Cognitive Digital Twins (CDTs) enhance DTs with semantic reasoning, explainability, and AI-driven decision support, yet they are typically difficult to integrate into distributed architectures. This paper proposes a Federated Cognitive Digital Twin (FCDT) architecture that combines federation and cognition within a unified approach. The architecture distributes intelligence across the edge-to-cloud continuum through local twins, which provide real-time monitoring and lightweight cognitive capabilities, and global twins, which perform system-level reasoning, simulation, and coordination. By integrating distributed autonomy with cognitive reasoning, the proposed approach improves scalability, responsiveness, and decision-making in complex distributed CPSs