mlsanityData to 7 Oct 2026

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

arXiv 1809.07857 ↗First posted 19 Sep 2018cs.NI cs.LGcited by 80

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Abstract (arXiv)

Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attentions from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate the content deliveries and improve the quality of mobile services. In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to the mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of "In-Edge AI".

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2018.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 23; 2022: 20; 2023: 18; 2024: 10; 2025: 7.

80 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  2. 2021Federated Reinforcement Learning: Techniques, Applications, and Open Challengescited by 79
  3. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  4. 2021Quantized Federated Learning under Transmission Delay and Outage Constraintscited by 37
  5. 2021Mobility-Aware Cluster Federated Learning in Hierarchical Wireless Networkscited by 35
  6. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  7. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  8. 2024Advances and Open Challenges in Federated Foundation Modelscited by 33
  9. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 29
  10. 2024Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learningcited by 29
  11. 2021Fusion of Federated Learning and Industrial Internet of Things: A Surveycited by 28
  12. 2022FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneitycited by 25
38 more
  1. 2023Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneitycited by 25
  2. 2021The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learningcited by 23
  3. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  4. 2020PFL-MoE: Personalized Federated Learning Based on Mixture of Expertscited by 21
  5. 2021Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challengescited by 17
  6. 2023Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Uglycited by 17
  7. 2024Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Networkcited by 17
  8. 2022Defending against the Label-flipping Attack in Federated Learningcited by 16
  9. 2024ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devicescited by 16
  10. 2021A Systematic Survey of Blockchained Federated Learningcited by 15
  11. 2022Deep Reinforcement Learning Assisted Federated Learning Algorithm for Data Management of IIoTcited by 15
  12. 2023A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Servicescited by 14
  13. 2022FL-Defender: Combating Targeted Attacks in Federated Learningcited by 12
  14. 2022Federated Continual Learning for Text Classification via Selective Inter-client Transfercited by 9
  15. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantizationcited by 9
  16. 2021A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networkscited by 7
  17. 2021Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learningcited by 7
  18. 2023Advancements in Federated Learning: Models, Methods, and Privacycited by 7
  19. 2021Compacting Deep Neural Networks for Internet of Things: Methods and Applicationscited by 6
  20. 2023A Comprehensive Survey On Client Selections in Federated Learningcited by 6
  21. 2024Why Go Full? Elevating Federated Learning Through Partial Network Updatescited by 6
  22. 2021Accelerating Edge Intelligence via Integrated Sensing and Communicationcited by 5
  23. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  24. 2023Have Your Cake and Eat It Too: Toward Efficient and Accurate Split Federated Learningcited by 5
  25. 2019Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Datacited by 4
  26. 2022Enabling All In-Edge Deep Learning: A Literature Reviewcited by 4
  27. 2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
  28. 2022Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Codingcited by 4
  29. 2023Asynchronous Wireless Federated Learning with Probabilistic Client Selectioncited by 4
  30. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  31. 2024Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networkscited by 4
  32. 2021Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Fastercited by 3
  33. 2022Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Samplingcited by 3
  34. 2022Federated clustering with GAN-based data synthesiscited by 3
  35. 2023FLEdge: Benchmarking Federated Machine Learning Applications in Edge Computing Systemscited by 3
  36. 2024HierSFL: Local Differential Privacy-aided Split Federated Learning in Mobile Edge Computingcited by 3
  37. 2021Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Servicescited by 2
  38. 2021Management of Resource at the Network Edge for Federated Learningcited by 2

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