mlsanityData to 14 Oct 2026

Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights

arXiv 2312.04688 ↗First posted 7 Dec 2023cs.LG cs.AI cs.GTcited by 4

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Abstract (arXiv)

Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of wireless systems, such as sixth-generation (6G) mobile network. However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices. Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. While there are a lot of survey papers exploring FL paradigms and usability in 6G privacy, none of them has clearly addressed how FL can be used to improve the protocol stack and wireless operations. The main goal of this survey is to provide a comprehensive overview on FL usability to enhance mobile services and enable smart ecosystems to support novel use-cases. This paper examines the added-value of implementing FL throughout all levels of the protocol stack. Furthermore, it presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry and sustain the development of cutting-edge mobile services.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 140 of its 312 references that are in the map. First posted on arXiv 7 Dec 2023.

Builds on

140 of 312 references.

References · 140

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  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
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38 more
  1. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 300
  2. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  3. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
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  9. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
  10. 2019A Survey on Distributed Machine Learningcited by 188
  11. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
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  15. 2019A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problemscited by 160
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  17. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 153
  18. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  19. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  20. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  21. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  22. 2018Blockchained On-Device Federated Learningcited by 106
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  24. 2018Towards an Intelligent Edge: Wireless Communication Meets Machine Learningcited by 95
  25. 2018Federated Learning for Keyword Spottingcited by 95
  26. 2020Collaborative Fairness in Federated Learningcited by 95
  27. 2022Semantic Communications for Future Internet: Fundamentals, Applications, and Challengescited by 95
  28. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 94
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  30. 2019Machine Learning at the Network Edge: A Surveycited by 84
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  32. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  33. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
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50 most cited of 140 All 140 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 0; 2026: 2.

4 papers.

  1. 2024A Zero Trust Framework for Realization and Defense Against Generative AI Attacks in Power Gridcited by 0
  2. 2024A Green Multi-Attribute Client Selection for Over-The-Air Federated Learning: A Grey-Wolf-Optimizer Approachcited by 0
  3. 2026FedEMA-Distill: Exponential Moving Average Guided Knowledge Distillation for Robust Federated Learningcited by 0
  4. 2026Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directionscited by 0