Distributed Data Intelligence in the Edge-to-Cloud Continuum: a Survey of Architectures, Models, and Applications

The exponential growth of data generated across edge devices, sensors, and cloud infrastructures has necessitated a paradigm shift in the distribution, processing, and action of intelligence. Distributed Data Intelligence (DDI) has emerged as a pivotal approach for enabling real-time, privacy-preserving, and scalable analytics across the edge-to-cloud continuum. This survey presents a comprehensive review of DDI systems focusing on their architectural foundations, computational models, and practical applications. It begins by establishing the conceptual underpinnings of the DDI and its evolution from traditional centralized paradigms. A detailed taxonomy of edge-fog-cloud architectures was introduced, highlighting orchestration mechanisms, virtualization technologies, and the role of federated learning. This study further examined lightweight models, hierarchical intelligence, and collaborative AI approaches tailored to distributed environments. Application domains such as smart healthcare, autonomous systems, industrial IoT, and smart cities were explored to illustrate the transformative potential of DDI. Finally, this survey identified critical challenges and open research questions, including interoperability, trust, sustainability, and governance in heterogeneously distributed ecosystems. By synthesizing insights from recent advancements, this study provides a structured foundation and a forward-looking perspective on the future of distributed intelligence in the edge-to-cloud era.