# A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks arXiv: https://arxiv.org/abs/2508.18803 ยท HTML: https://mlsanity.com/paper/2508.18803/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 160 of its 289 references that are in the map. - First posted on arXiv 26 Aug 2025. ## Builds on (most cited first, 30 of 160) - [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020) - [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024) - [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020) - [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023) - [Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization](https://mlsanity.com/paper/2308.05384/) (2023) - [SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification](https://mlsanity.com/paper/2112.06274/) (2021) - [When Large Language Model Agents Meet 6G Networks: Perception, Grounding, and Alignment](https://mlsanity.com/paper/2401.07764/) (2024) - [Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI](https://mlsanity.com/paper/2111.06061/) (2021) - [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019) - [Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge](https://mlsanity.com/paper/2312.05693/) (2023) - [Auto-Split: A General Framework of Collaborative Edge-Cloud AI](https://mlsanity.com/paper/2108.13041/) (2021) - [MMST-ViT: Climate Change-aware Crop Yield Prediction via Multi-Modal Spatial-Temporal Vision Transformer](https://mlsanity.com/paper/2309.09067/) (2023) - [Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors](https://mlsanity.com/paper/2112.06872/) (2021) - [Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy](https://mlsanity.com/paper/2007.00914/) (2020) - [Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning](https://mlsanity.com/paper/2012.08677/) (2020) - [Asynchronous Federated Learning with Differential Privacy for Edge Intelligence](https://mlsanity.com/paper/1912.07902/) (2019) - [Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare](https://mlsanity.com/paper/2111.12241/) (2021) - [Cached Model-as-a-Resource: Provisioning Large Language Model Agents for Edge Intelligence in Space-air-ground Integrated Networks](https://mlsanity.com/paper/2403.05826/) (2024) - [Detecting DDoS Attack on SDN Due to Vulnerabilities in OpenFlow](https://mlsanity.com/paper/1912.12221/) (2019) - [Remote patient monitoring using artificial intelligence: Current state, applications, and challenges](https://mlsanity.com/paper/2301.10009/) (2023) - [Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey](https://mlsanity.com/paper/2505.01821/) (2025) - [Resource Management in Fog/Edge Computing: A Survey](https://mlsanity.com/paper/1810.00305/) (2018) - [Arena: A Learning-based Synchronization Scheme for Hierarchical Federated Learning--Technical Report](https://mlsanity.com/paper/2308.10298/) (2023) - [FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT](https://mlsanity.com/paper/2012.06150/) (2020) - [Cloud-Device Collaborative Learning for Multimodal Large Language Models](https://mlsanity.com/paper/2312.16279/) (2023) - [Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation](https://mlsanity.com/paper/2402.17316/) (2024) - [VELO: A Vector Database-Assisted Cloud-Edge Collaborative LLM QoS Optimization Framework](https://mlsanity.com/paper/2406.13399/) (2024) - [A Survey on Security and Privacy Issues in Edge Computing-Assisted Internet of Things](https://mlsanity.com/paper/2008.03252/) (2020) - [Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things](https://mlsanity.com/paper/2012.04436/) (2020) - [TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security](https://mlsanity.com/paper/2205.01232/) (2022) ## Built on by (most cited first, 3 of 3) - [Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity](https://mlsanity.com/paper/2603.19337/) (2026) - [Collaborative Adaptive Curriculum for Progressive Knowledge Distillation](https://mlsanity.com/paper/2603.20296/) (2026) - [Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuning](https://mlsanity.com/paper/2607.12112/) (2026) ## Abstract (arXiv) 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 review for CISAIOT beginners. We systematically analyze architectural components spanning cloud, edge, and terminal layers, examining core technologies including network virtualization, container orchestration, and software-defined networking, while presenting categorizations of collaboration paradigms that cover task offloading, resource allocation, and optimization across heterogeneous infrastructures. Furthermore, we explain intelligent collaboration learning frameworks by reviewing advances in federated learning, distributed deep learning, edge-cloud model evolution, and reinforcement learning-based methods. Finally, we discuss challenges (e.g., scalability, heterogeneity, interoperability) and future trends (e.g., 6G+, agents, quantum computing, digital twin), highlighting how integration of distributed computing and communication can address open issues and guide development of robust, efficient, and secure collaborative AIoT systems.