mlsanityData to 7 Oct 2026

Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges

arXiv 2111.07392 ↗First posted 14 Nov 2021cs.NI cs.AIcited by 12

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Abstract (arXiv)

New technological advancements in wireless networks have enlarged the number of connected devices. The unprecedented surge of data volume in wireless systems empowered by artificial intelligence (AI) opens up new horizons for providing ubiquitous data-driven intelligent services. Traditional cloudcentric machine learning (ML)-based services are implemented by centrally collecting datasets and training models. However, this conventional training technique encompasses two challenges: (i) high communication and energy cost and (ii) threatened data privacy. In this article, we introduce a comprehensive survey of the fundamentals and enabling technologies of federated learning (FL), a newly emerging technique coined to bring ML to the edge of wireless networks. Moreover, an extensive study is presented detailing various applications of FL in wireless networks and highlighting their challenges and limitations. The efficacy of FL is further explored with emerging prospective beyond fifth-generation (B5G) and sixth-generation (6G) communication systems. This survey aims to provide an overview of the state-ofthe-art FL applications in key wireless technologies that will serve as a foundation to establish a firm understanding of the topic. Lastly, we offer a road forward for future research directions.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 110 of its 262 references that are in the map. First posted on arXiv 14 Nov 2021.

Builds on

110 of 262 references.

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Cited by year

Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 4.

12 papers.

  1. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  2. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  3. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  4. 2025Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontierscited by 3
  5. 2023Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognitioncited by 2
  6. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  7. 2025Two Stage Wireless Federated LoRA Fine-Tuning with Sparsified Orthogonal Updatescited by 2
  8. 2023TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistancecited by 1
  9. 2024Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Samecited by 1
  10. 2024FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillationcited by 1
  11. 2022FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Gridscited by 0
  12. 20256G-Enabled Digital Twin Framework for Real-Time Cyber-Physical Systems: An Experimental Validation with Industrial Bearing Fault Detectioncited by 0