mlsanityData to 7 Oct 2026

Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

arXiv 1905.10083 ↗First posted 24 May 2019cs.DC cs.AI cs.NIcited by 225

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Abstract (arXiv)

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet-of-Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions Bytes of data at the network edge. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To meet this demand, edge computing, an emerging paradigm that pushes computing tasks and services from the network core to the network edge, has been widely recognized as a promising solution. The resulted new inter-discipline, edge AI or edge intelligence, is beginning to receive a tremendous amount of interest. However, research on edge intelligence is still in its infancy stage, and a dedicated venue for exchanging the recent advances of edge intelligence is highly desired by both the computer system and artificial intelligence communities. To this end, we conduct a comprehensive survey of the recent research efforts on edge intelligence. Specifically, we first review the background and motivation for artificial intelligence running at the network edge. We then provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training/inference at the network edge. Finally, we discuss future research opportunities on edge intelligence. We believe that this survey will elicit escalating attentions, stimulate fruitful discussions and inspire further research ideas on edge intelligence.

Key facts. Cited by 225 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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Citing papers per year: 2021: 34; 2022: 40; 2023: 40; 2024: 47; 2025: 34; 2026: 30.

225 papers.

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  1. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 125
  2. 2025Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonablecited by 120
  3. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  4. 2021Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysiscited by 51
  5. 2021Task-Oriented Communication for Multi-Device Cooperative Edge Inferencecited by 47
  6. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
  7. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  8. 2023SiDA-MoE: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Modelscited by 36
  9. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  10. 2021FedAdapt: Adaptive Offloading for IoT Devices in Federated Learningcited by 26
  11. 2024Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inferencecited by 25
  12. 2023Green Edge AI: A Contemporary Surveycited by 24
38 more
  1. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 23
  2. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  3. 2023FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUscited by 20
  4. 2021Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inferencecited by 19
  5. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  6. 2021Multi-Exit Semantic Segmentation Networkscited by 18
  7. 2022The Cost of Training Machine Learning Models over Distributed Data Sourcescited by 18
  8. 2024PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMscited by 18
  9. 2022A Feature Memory Rearrangement Network for Visual Inspection of Textured Surface Defects Toward Edge Intelligent Manufacturingcited by 16
  10. 2023Accelerating Split Federated Learning over Wireless Communication Networkscited by 16
  11. 2023Holistic Network Virtualization and Pervasive Network Intelligence for 6Gcited by 14
  12. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  13. 2021Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-designcited by 13
  14. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  15. 2022Task-Oriented Communication for Edge Video Analyticscited by 13
  16. 2024Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devicescited by 13
  17. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  18. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 12
  19. 2022Predictive GAN-powered Multi-Objective Optimization for Hybrid Federated Split Learningcited by 11
  20. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  21. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  22. 2024Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillationcited by 11
  23. 2021Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfercited by 10
  24. 2021DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systemscited by 10
  25. 2022Resource-Constrained Edge AI with Early Exit Predictioncited by 10
  26. 2022Device-Cloud Collaborative Recommendation via Meta Controllercited by 10
  27. 2022FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edgecited by 10
  28. 2023MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updatescited by 10
  29. 2025SpecEdge: Scalable Edge-Assisted Serving Framework for Interactive LLMscited by 10
  30. 2021Relay-Assisted Cooperative Federated Learningcited by 9
  31. 2022Decentralized Low-Latency Collaborative Inference via Ensembles on the Edgecited by 9
  32. 2024Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Surveycited by 9
  33. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  34. 2024Benchmarking Edge AI Platforms for High-Performance ML Inferencecited by 9
  35. 2025DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance Analysiscited by 9
  36. 2025Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guaranteescited by 9
  37. 2021Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimizationcited by 8
  38. 2023Edge-Based Video Analytics: A Surveycited by 8

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