mlsanityData to 7 Oct 2026

Edge Artificial Intelligence: A Systematic Review of Evolution, Taxonomic Frameworks, and Future Horizons

arXiv 2510.01439 ↗First posted 1 Oct 2025cs.LGcited by 2

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Abstract (arXiv)

Edge Artificial Intelligence (Edge AI) embeds intelligence directly into devices at the network edge, enabling real-time processing with improved privacy and reduced latency by processing data close to its source. This review systematically examines the evolution, current landscape, and future directions of Edge AI through a multi-dimensional taxonomy including deployment location, processing capabilities such as TinyML and federated learning, application domains, and hardware types. Following PRISMA guidelines, the analysis traces the field from early content delivery networks and fog computing to modern on-device intelligence. Core enabling technologies such as specialized hardware accelerators, optimized software, and communication protocols are explored. Challenges including resource limitations, security, model management, power consumption, and connectivity are critically assessed. Emerging opportunities in neuromorphic hardware, continual learning algorithms, edge-cloud collaboration, and trustworthiness integration are highlighted, providing a comprehensive framework for researchers and practitioners.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 31 of its 129 references that are in the map. First posted on arXiv 1 Oct 2025.

Builds on

31 of 129 references.

References · 31

  1. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
  2. 2024On-Device Language Models: A Comprehensive Reviewcited by 101
  3. 2021Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applicationscited by 96
  4. 2020Communication-Efficient Edge AI: Algorithms and Systemscited by 95
  5. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  6. 2024Fast On-device LLM Inference with NPUscited by 58
  7. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 44
  8. 2024Tiny Machine Learning: Progress and Futurescited by 40
  9. 2023Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Servicescited by 33
  10. 2025Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latencycited by 32
  11. 2024Edge AI: A Taxonomy, Systematic Review and Future Directionscited by 21
  12. 2021TinyML for Ubiquitous Edge AIcited by 13
19 more
  1. 2024Designing Efficient LLM Accelerators for Edge Devicescited by 13
  2. 2022Resource-Constrained Edge AI with Early Exit Predictioncited by 10
  3. 2024Benchmarking Edge AI Platforms for High-Performance ML Inferencecited by 9
  4. 2023Enhancing Neural Architecture Search with Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devicescited by 7
  5. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  6. 2024XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approachcited by 7
  7. 2022A New Frontier of AI: On-Device AI Training and Personalizationcited by 5
  8. 2022A Survey on Mobile Edge Computing for Video Streaming: Opportunities and Challengescited by 5
  9. 2024Collaborative Edge AI Inference over Cloud-RANcited by 5
  10. 2025Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmarkcited by 5
  11. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5
  12. 2020A Demonstration of Smart Doorbell Design Using Federated Deep Learningcited by 2
  13. 2021Data Collection and Utilization Framework for Edge AI Applicationscited by 2
  14. 2023A Decade of Research in Fog computing: Relevance, Challenges, and Future Directionscited by 2
  15. 2025Advancements in Mobile Edge Computing and Open RAN: Leveraging Artificial Intelligence and Machine Learning for Wireless Systemscited by 2
  16. 2023Fog Computing& IoT: Overview, Architecture and Applicationscited by 1
  17. 2024SHA-CNN: Scalable Hierarchical Aware Convolutional Neural Network for Edge AIcited by 1
  18. 2024EdgeShield: A Universal and Efficient Edge Computing Framework for Robust AIcited by 1
  19. 2025Edge AI-Powered Real-Time Decision-Making for Autonomous Vehicles in Adverse Weather Conditionscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Affordable Precision Agriculture: A Deployment-Oriented Review of Low-Cost, Low-Power Edge AI and TinyML for Resource-Constrained Farming Systemscited by 0
  2. 2026From Digital Twins to World Models:Opportunities, Challenges, and Applications for Mobile Edge General Intelligencecited by 0