mlsanityData to 7 Oct 2026

Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trends

arXiv 2105.00842 ↗First posted 24 Apr 2021cs.DC cs.LGcited by 6

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Abstract (arXiv)

Conventional machine learning techniques are conducted in a centralized manner. Recently, the massive volume of generated wireless data, the privacy concerns and the increasing computing capabilities of wireless end-devices have led to the emergence of a promising decentralized solution, termed as Wireless Federated Learning (WFL). In this first of the two parts paper, we present the application of WFL in the sixth generation of wireless networks (6G), which is envisioned to be an integrated communication and computing platform. After analyzing the key concepts of WFL, we discuss the core challenges of WFL imposed by the wireless (or mobile communication) environment. Finally, we shed light to the future directions of WFL, aiming to compose a constructive integration of FL into the future wireless networks.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 21 references that are in the map. First posted on arXiv 24 Apr 2021.

Builds on

13 of 21 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Deep Learning with Differential Privacycited by 2,682
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  4. 2019Asynchronous Federated Optimizationcited by 264
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  6. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  7. 2019The Roadmap to 6G -- AI Empowered Wireless Networkscited by 150
  8. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  9. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  10. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  11. 2019Energy Demand Prediction with Federated Learning for Electric Vehicle Networkscited by 46
  12. 2021Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?cited by 13
1 more
  1. 2015Big Data Analytics for Dynamic Energy Management in Smart Gridscited by 3

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Cited by year

Citing papers per year: 2022: 2; 2023: 1; 2024: 0; 2025: 3.

6 papers.

  1. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Designcited by 15
  2. 2025Unified Analysis of Decentralized Gradient Descent: a Contraction Mapping Frameworkcited by 10
  3. 2022Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Realitycited by 9
  4. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  5. 2025Principles and Components of Federated Learning Architecturescited by 0
  6. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0