# Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence

arXiv: https://arxiv.org/abs/1909.00560 · HTML: https://mlsanity.com/paper/1909.00560/

## Key facts

- Cited by 76 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2019.

## Built on by (most cited first, 30 of 76)

- [Mobile Edge Intelligence for Large Language Models: A Contemporary Survey](https://mlsanity.com/paper/2407.18921/) (2024)
- [Continual Learning and Catastrophic Forgetting](https://mlsanity.com/paper/2403.05175/) (2024)
- [Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models](https://mlsanity.com/paper/2503.06027/) (2025)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [MP-FedCL: Multiprototype Federated Contrastive Learning for Edge Intelligence](https://mlsanity.com/paper/2304.01950/) (2023)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [The Cost of Training Machine Learning Models over Distributed Data Sources](https://mlsanity.com/paper/2209.07124/) (2022)
- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Fusion of Global and Local Knowledge for Personalized Federated Learning](https://mlsanity.com/paper/2302.11051/) (2023)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learning](https://mlsanity.com/paper/2209.02428/) (2022)
- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [Trustless Autonomy: Understanding Motivations, Benefits, and Governance Dilemmas in Self-Sovereign Decentralized AI Agents](https://mlsanity.com/paper/2505.09757/) (2025)
- [Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT](https://mlsanity.com/paper/2110.14937/) (2021)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Time-sensitive Learning for Heterogeneous Federated Edge Intelligence](https://mlsanity.com/paper/2301.10977/) (2023)
- [RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS](https://mlsanity.com/paper/2408.08699/) (2024)
- [Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques](https://mlsanity.com/paper/2211.15751/) (2022)
- [Model Pruning Based on Quantified Similarity of Feature Maps](https://mlsanity.com/paper/2105.06052/) (2021)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [Layered Randomized Quantization for Communication-Efficient and Privacy-Preserving Distributed Learning](https://mlsanity.com/paper/2312.07060/) (2023)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping](https://mlsanity.com/paper/2112.13222/) (2021)
- [Towards Energy-Aware Federated Traffic Prediction for Cellular Networks](https://mlsanity.com/paper/2309.10645/) (2023)
- [Towards Message Brokers for Generative AI: Survey, Challenges, and Opportunities](https://mlsanity.com/paper/2312.14647/) (2023)
- [Mixture-of-Experts for Distributed Edge Computing with Channel-Aware Gating Function](https://mlsanity.com/paper/2504.00819/) (2025)
- [ComBodied Agents: a New Paradigm of Human-Centric Agentic AI](https://mlsanity.com/paper/2608.10915/) (2026)
- [Enabling All In-Edge Deep Learning: A Literature Review](https://mlsanity.com/paper/2204.03326/) (2022)
- [Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques](https://mlsanity.com/paper/2502.13256/) (2025)
- [Over-the-Air Semantic Alignment with Stacked Intelligent Metasurfaces](https://mlsanity.com/paper/2512.05657/) (2025)

## Abstract (arXiv)

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.