# Federated Learning and Wireless Communications

arXiv: https://arxiv.org/abs/2005.05265 · HTML: https://mlsanity.com/paper/2005.05265/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 27 of 27)

- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications](https://mlsanity.com/paper/2204.02321/) (2022)
- [Convergence Acceleration in Wireless Federated Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2209.06623/) (2022)
- [Efficient Wireless Federated Learning with Partial Model Aggregation](https://mlsanity.com/paper/2204.09746/) (2022)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [Federated Learning for Physical Layer Design](https://mlsanity.com/paper/2102.11777/) (2021)
- [Robust Federated Learning via Over-The-Air Computation](https://mlsanity.com/paper/2111.01221/) (2021)
- [Compressed Particle-Based Federated Bayesian Learning and Unlearning](https://mlsanity.com/paper/2209.07267/) (2022)
- [Over-The-Air Federated Learning under Byzantine Attacks](https://mlsanity.com/paper/2205.02949/) (2022)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Federated Learning for Metaverse: A Survey](https://mlsanity.com/paper/2303.17987/) (2023)
- [Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO](https://mlsanity.com/paper/2212.06482/) (2022)
- [Rethinking Clustered Federated Learning in NOMA Enhanced Wireless Networks](https://mlsanity.com/paper/2403.03157/) (2024)
- [FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning](https://mlsanity.com/paper/2506.07581/) (2025)
- [Exact Penalty Method for Federated Learning](https://mlsanity.com/paper/2208.11231/) (2022)
- [Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation](https://mlsanity.com/paper/2308.04953/) (2023)
- [Communication-Efficient and Differentially Private Vertical Federated Learning with Zeroth-Order Optimization](https://mlsanity.com/paper/2502.20565/) (2025)
- [Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems](https://mlsanity.com/paper/2506.03586/) (2025)
- [CA-HFP: Curvature-Aware Heterogeneous Federated Pruning with Model Reconstruction](https://mlsanity.com/paper/2603.12591/) (2026)
- [Federated Adversarial Training with Transformers](https://mlsanity.com/paper/2206.02131/) (2022)
- [Efficient Adaptive Federated Optimization of Federated Learning for IoT](https://mlsanity.com/paper/2206.11448/) (2022)
- [Optimisation of federated learning settings under statistical heterogeneity variations](https://mlsanity.com/paper/2406.06340/) (2024)
- [Small-Scale-Fading-Aware Resource Allocation in Wireless Federated Learning](https://mlsanity.com/paper/2505.03533/) (2025)
- [Privacy-Preserving Quantized Federated Learning with Diverse Precision](https://mlsanity.com/paper/2507.00920/) (2025)
- [Event-Triggered Gossip for Distributed Learning](https://mlsanity.com/paper/2602.19116/) (2026)
- [Federated Multi Agent Deep Learning and Neural Networks for Advanced Distributed Sensing in Wireless Networks](https://mlsanity.com/paper/2603.16881/) (2026)

## 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.