mlsanityData to 14 Oct 2026

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

arXiv 2111.12444 ↗First posted 24 Nov 2021cs.IT cs.LG cs.NIcited by 97

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Abstract (arXiv)

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from "connected things" to "connected intelligence". However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems.

Key facts. Cited by 97 papers in the map (data through 14 Oct 2026). Builds on 181 of its 325 references that are in the map. First posted on arXiv 24 Nov 2021.

Builds on

181 of 325 references.

References · 181

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50 most cited of 181 All 181 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 21; 2023: 22; 2024: 20; 2025: 25; 2026: 9.

97 papers.

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  1. 2023Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networkscited by 95
  2. 2024AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networkscited by 64
  3. 2022Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learningcited by 59
  4. 2023Large Language Models Empowered Autonomous Edge AI for Connected Intelligencecited by 56
  5. 2022Graph Neural Networks for Wireless Communications: From Theory to Practicecited by 46
  6. 2024AI Flow at the Network Edgecited by 46
  7. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 46
  8. 2025AI Flow: Perspectives, Scenarios, and Approachescited by 44
  9. 2023FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networkscited by 38
  10. 2022Deep Learning-Enabled Semantic Communication Systems with Task-Unaware Transmitter and Dynamic Datacited by 36
  11. 2022Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AIcited by 33
  12. 2023A Survey on Vertical Federated Learning: From a Layered Perspectivecited by 30
38 more
  1. 2022Robust Information Bottleneck for Task-Oriented Communication with Digital Modulationcited by 29
  2. 2023Green Edge AI: A Contemporary Surveycited by 25
  3. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 24
  4. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  5. 2023Communication-Efficient Split Learning via Adaptive Feature-Wise Compressioncited by 18
  6. 2025Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Modelcited by 16
  7. 2022Trustworthy Federated Learning via Blockchaincited by 15
  8. 2022State-Augmented Learnable Algorithms for Resource Management in Wireless Networkscited by 14
  9. 2022Task-Oriented Over-the-Air Computation for Multi-Device Edge AIcited by 14
  10. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 14
  11. 2022Task-Oriented Communication for Edge Video Analyticscited by 13
  12. 2024Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approachcited by 12
  13. 2024A Survey on Integrated Sensing, Communication, and Computationcited by 12
  14. 2022Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learningcited by 11
  15. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  16. 2023Adaptive Resource Allocation for Semantic Communication Networkscited by 10
  17. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  18. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectoriescited by 9
  19. 2023Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systemscited by 8
  20. 2024Peer-to-Peer Learning Dynamics of Wide Neural Networkscited by 8
  21. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  22. 2023When Decentralized Optimization Meets Federated Learningcited by 7
  23. 2023A Survey on Trustworthy Edge Intelligence: From Security and Reliability To Transparency and Sustainabilitycited by 7
  24. 2025Adaptive Token Merging for Efficient Transformer Semantic Communication at the Edgecited by 7
  25. 2023Performance Limits of a Deep Learning-Enabled Text Semantic Communication under Interferencecited by 6
  26. 2024Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMscited by 6
  27. 2022Differentially Private Federated Learning via Reconfigurable Intelligent Surfacecited by 5
  28. 2022In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networkscited by 5
  29. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  30. 2023Efficient Multiuser AI Downloading via Reusable Knowledge Broadcastingcited by 5
  31. 2024Collaborative Edge AI Inference over Cloud-RANcited by 5
  32. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
  33. 2022Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Controlcited by 4
  34. 2023Integrated Sensing-Communication-Computation for Edge Artificial Intelligencecited by 4
  35. 2024Federated Learning With Energy Harvesting Devices: An MDP Frameworkcited by 4
  36. 2024Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edgecited by 4
  37. 2025Optimal Expert Selection for Distributed Mixture-of-Experts at the Wireless Edgecited by 4
  38. 2024Two-Timescale Synchronization and Migration for Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Approachcited by 3

50 most cited of 97 All 97 (JSON)