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

arXiv: https://arxiv.org/abs/2111.07392 · HTML: https://mlsanity.com/paper/2111.07392/

## Key facts

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

## Builds on (most cited first, 30 of 110)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Perceiver: General Perception with Iterative Attention](https://mlsanity.com/paper/2103.03206/) (2021)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Channel Pruning for Accelerating Very Deep Neural Networks](https://mlsanity.com/paper/1707.06168/) (2017)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training](https://mlsanity.com/paper/1712.01887/) (2017)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Personalized Federated Learning: A Meta-Learning Approach](https://mlsanity.com/paper/2002.07948/) (2020)
- [Random Search and Reproducibility for Neural Architecture Search](https://mlsanity.com/paper/1902.07638/) (2019)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)

## Built on by (most cited first, 12 of 12)

- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Data](https://mlsanity.com/paper/2501.09822/) (2025)
- [Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers](https://mlsanity.com/paper/2511.22616/) (2025)
- [Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition](https://mlsanity.com/paper/2307.06834/) (2023)
- [UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis](https://mlsanity.com/paper/2407.07739/) (2024)
- [Two Stage Wireless Federated LoRA Fine-Tuning with Sparsified Orthogonal Updates](https://mlsanity.com/paper/2505.00333/) (2025)
- [TrustFed: A Reliable Federated Learning Framework with Malicious-Attack Resistance](https://mlsanity.com/paper/2312.04597/) (2023)
- [Dynamic and Super-Personalized Media Ecosystem Driven by Generative AI: Unpredictable Plays Never Repeating The Same](https://mlsanity.com/paper/2402.12412/) (2024)
- [FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation](https://mlsanity.com/paper/2405.13378/) (2024)
- [FedTrees: A Novel Computation-Communication Efficient Federated Learning Framework Investigated in Smart Grids](https://mlsanity.com/paper/2210.00060/) (2022)
- [6G-Enabled Digital Twin Framework for Real-Time Cyber-Physical Systems: An Experimental Validation with Industrial Bearing Fault Detection](https://mlsanity.com/paper/2510.03807/) (2025)

## 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.