A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks
Jiaqi Wu, Jing Liu, Yang Liu, Lixu Wang, Zehua Wang, Wei Chen, Zijian Tian, Richard Yu, Victor C.M. Leung

TL;DR
This survey reviews the architectures, technologies, and collaboration paradigms enabling cloud-edge-terminal intelligence in AIoT networks, highlighting recent advances, challenges, and future directions for distributed AI applications.
Contribution
It provides a comprehensive tutorial-style overview of CETCI architectures, core technologies, and intelligent collaboration frameworks in AIoT, integrating recent research and future trends.
Findings
Analyzes architectural components across cloud, edge, and terminal layers.
Reviews advances in federated learning, distributed deep learning, and reinforcement learning.
Discusses challenges like scalability, heterogeneity, and interoperability.
Abstract
The proliferation of Internet of things (IoT) devices in smart cities, transportation, healthcare, and industrial applications, coupled with the explosive growth of AI-driven services, has increased demands for efficient distributed computing architectures and networks, driving cloud-edge-terminal collaborative intelligence (CETCI) as a fundamental paradigm within the artificial intelligence of things (AIoT) community. With advancements in deep learning, large language models (LLMs), and edge computing, CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems for AIoT (CISAIOT), a practical research focus in AI, distributed computing, and communications. This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style…
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