mlsanityData to 14 Oct 2026

Multiple Access in the Era of Distributed Computing and Edge Intelligence

arXiv 2403.07903 ↗First posted 26 Feb 2024cs.NI cs.LG eess.SPcited by 4

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Abstract (arXiv)

This paper focuses on the latest research and innovations in fundamental next-generation multiple access (NGMA) techniques and the coexistence with other key technologies for the sixth generation (6G) of wireless networks. In more detail, we first examine multi-access edge computing (MEC), which is critical to meeting the growing demand for data processing and computational capacity at the edge of the network, as well as network slicing. We then explore over-the-air (OTA) computing, which is considered to be an approach that provides fast and efficient computation of various functions. We also explore semantic communications, identified as an effective way to improve communication systems by focusing on the exchange of meaningful information, thus minimizing unnecessary data and increasing efficiency. The interrelationship between machine learning (ML) and multiple access technologies is also reviewed, with an emphasis on federated learning, federated distillation, split learning, reinforcement learning, and the development of ML-based multiple access protocols. Finally, the concept of digital twinning and its role in network management is discussed, highlighting how virtual replication of physical networks can lead to improvements in network efficiency and reliability.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 85 of its 187 references that are in the map. First posted on arXiv 26 Feb 2024.

Builds on

85 of 187 references.

References · 85

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  3. 2020Deep Learning Enabled Semantic Communication Systemscited by 292
  4. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
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  12. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
38 more
  1. 2021Semantic Communication Systems for Speech Transmissioncited by 97
  2. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  3. 2019Detailed comparison of communication efficiency of split learning and federated learningcited by 77
  4. 2022Split Learning over Wireless Networks: Parallel Design and Resource Managementcited by 75
  5. 2020A Lite Distributed Semantic Communication System for Internet of Thingscited by 73
  6. 2018Multi-Agent Deep Reinforcement Learning for Dynamic Power Allocation in Wireless Networkscited by 70
  7. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  8. 2022Generative Joint Source-Channel Coding for Semantic Image Transmissioncited by 58
  9. 2020Toward a 6G AI-Native Air Interfacecited by 55
  10. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
  11. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  12. 2022Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunitiescited by 40
  13. 2022DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Codingcited by 38
  14. 2017Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networkscited by 37
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  16. 2022Performance Optimization for Semantic Communications: An Attention-based Reinforcement Learning Approachcited by 28
  17. 2019Over-the-Air Computation Systems: Optimization, Analysis and Scaling Lawscited by 27
  18. 2020Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmissioncited by 23
  19. 2022A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networkscited by 22
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  22. 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
  23. 2020Semantic Communications for Speech Signalscited by 19
  24. 2019A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Artcited by 18
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  27. 2019Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn from a Digital Twincited by 17
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  38. 2022Communication-Efficient Federated Distillation with Active Data Samplingcited by 10

50 most cited of 85 All 85 (JSON)

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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Split Learning in Computer Vision for Semantic Segmentation Delay Minimizationcited by 2
  2. 2025Federated Learning-Distillation Alternation for Resource-Constrained IoTcited by 2
  3. 2024Waveform Design for Over-the-Air Computingcited by 0
  4. 2025Heterogeneous Resource Allocation with Multi-task Learning for Wireless Networkscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Latency and Energy Minimization in NOMA-Assisted MEC Network: A Federated Deep Reinforcement Learning Approach ↗
  2. 2026A Multi-Prototype-Guided Federated Knowledge Distillation Approach in AI-RAN Enabled Multi-Access Edge Computing System
  3. 2024Massive Digital Over-the-Air Computation for Communication-Efficient Federated Edge Learning
  4. 2023Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning
  5. 2026The Computing Channel: How Modulation Programs the Airwaves ↗
  6. 2026Toward Efficient Deployment and Synchronization in Digital Twins-Empowered Networks
  7. 2022CFLIT: Coexisting Federated Learning and Information Transfer
  8. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing