# UAV Communications for Sustainable Federated Learning

arXiv: https://arxiv.org/abs/2103.11073 · HTML: https://mlsanity.com/paper/2103.11073/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 20 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms](https://mlsanity.com/paper/2007.12004/) (2020)
- [Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory](https://mlsanity.com/paper/2003.04451/) (2020)
- [A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data](https://mlsanity.com/paper/1511.02746/) (2015)
- [Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems](https://mlsanity.com/paper/1806.04589/) (2018)
- [Energy Efficient UAV Communication with Energy Harvesting](https://mlsanity.com/paper/1911.00351/) (2019)
- [Lightwave Power Transfer for Federated Learning-based Wireless Networks](https://mlsanity.com/paper/2005.03977/) (2020)

## Built on by (most cited first, 15 of 15)

- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehicles](https://mlsanity.com/paper/2203.05192/) (2022)
- [Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learning](https://mlsanity.com/paper/2107.07233/) (2021)
- [Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions](https://mlsanity.com/paper/2110.04160/) (2021)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learning](https://mlsanity.com/paper/2205.11779/) (2022)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation](https://mlsanity.com/paper/2412.07428/) (2024)
- [Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions](https://mlsanity.com/paper/2211.01036/) (2022)
- [Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2411.08918/) (2024)
- [Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocation](https://mlsanity.com/paper/2308.04953/) (2023)
- [Multi-task Federated Edge Learning (MtFEEL) in Wireless Networks](https://mlsanity.com/paper/2108.02517/) (2021)
- [Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networks](https://mlsanity.com/paper/2110.07077/) (2021)
- [Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networks](https://mlsanity.com/paper/2304.06551/) (2023)
- [Latency-aware Multimodal Federated Learning over UAV Networks](https://mlsanity.com/paper/2510.01717/) (2025)

## Abstract (arXiv)

Federated learning (FL), invented by Google in 2016, has become a hot research trend. However, enabling FL in wireless networks has to overcome the limited battery challenge of mobile users. In this regard, we propose to apply unmanned aerial vehicle (UAV)-empowered wireless power transfer to enable sustainable FL-based wireless networks. The objective is to maximize the UAV transmit power efficiency, via a joint optimization of transmission time and bandwidth allocation, power control, and the UAV placement. Directly solving the formulated problem is challenging, due to the coupling of variables. Hence, we leverage the decomposition technique and a successive convex approximation approach to develop an efficient algorithm, namely UAV for sustainable FL (UAV-SFL). Finally, simulations illustrate the potential of our proposed UAV-SFL approach in providing a sustainable solution for FL-based wireless networks, and in reducing the UAV transmit power by 32.95%, 63.18%, and 78.81% compared with the benchmarks.