mlsanityData to 7 Oct 2026

Federated Learning and Wireless Communications

arXiv 2005.05265 ↗First posted 11 May 2020cs.IT eess.SP math.ITcited by 27

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Abstract (arXiv)

Federated learning becomes increasingly attractive in the areas of wireless communications and machine learning due to its powerful functions and potential applications. In contrast to other machine learning tools that require no communication resources, federated learning exploits communications between the central server and the distributed local clients to train and optimize a machine learning model. Therefore, how to efficiently assign limited communication resources to train a federated learning model becomes critical to performance optimization. On the other hand, federated learning, as a brand new tool, can potentially enhance the intelligence of wireless networks. In this article, we provide a comprehensive overview on the relationship between federated learning and wireless communications, including basic principle of federated learning, efficient communications for training a federated learning model, and federated learning for intelligent wireless applications. We also identify some future research challenges and directions at the end of this article.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 11; 2023: 3; 2024: 2; 2025: 5; 2026: 3.

27 papers.

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  1. 2022Federated Learning via Inexact ADMMcited by 44
  2. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  3. 2022SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communicationscited by 11
  4. 2022Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approachcited by 10
  5. 2022Efficient Wireless Federated Learning with Partial Model Aggregationcited by 8
  6. 2022FedGiA: An Efficient Hybrid Algorithm for Federated Learningcited by 8
  7. 2021Federated Learning for Physical Layer Designcited by 6
  8. 2021Robust Federated Learning via Over-The-Air Computationcited by 5
  9. 2022Compressed Particle-Based Federated Bayesian Learning and Unlearningcited by 5
  10. 2022Over-The-Air Federated Learning under Byzantine Attackscited by 4
  11. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  12. 2023Federated Learning for Metaverse: A Surveycited by 3
15 more
  1. 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
  2. 2024Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networkscited by 2
  3. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  4. 2022Exact Penalty Method for Federated Learningcited by 1
  5. 2023Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocationcited by 1
  6. 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systemscited by 1
  7. 2026CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstructioncited by 1
  8. 2022Federated Adversarial Training with Transformerscited by 0
  9. 2022Efficient Adaptive Federated Optimization of Federated Learning for IoTcited by 0
  10. 2024Optimisation of federated learning settings under statistical heterogeneity variationscited by 0
  11. 2025Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimizationcited by 0
  12. 2025Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learningcited by 0
  13. 2025Privacy-Preserving Quantized Federated Learning with Diverse Precisioncited by 0
  14. 2026Event-Triggered Gossip for Distributed Learningcited by 0
  15. 2026Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networkscited by 0