mlsanityData to 7 Oct 2026

UAV Communications for Sustainable Federated Learning

arXiv 2103.11073 ↗First posted 20 Mar 2021cs.NI cs.LGcited by 15

Not on the map. Why?

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 20 Mar 2021.

Builds on

12 of 19 references.

References · 12

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  4. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 202
  5. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  6. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  7. 2020Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarmscited by 26
  8. 2020Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theorycited by 16
  9. 2015A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Datacited by 15
  10. 2018Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systemscited by 8
  11. 2019Energy Efficient UAV Communication with Energy Harvestingcited by 6
  12. 2020Lightwave Power Transfer for Federated Learning-based Wireless Networkscited by 5

Built on by

Cited by year

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

15 papers.

Sort:

  1. 2021Federated Learning for Smart Healthcare: A Surveycited by 182
  2. 2022A Systematic Literature Review on Blockchain Enabled Federated Learning Framework for Internet of Vehiclescited by 7
  3. 2021Genetic CFL: Optimization of Hyper-Parameters in Clustered Federated Learningcited by 6
  4. 2021Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directionscited by 6
  5. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  6. 2022Wireless Ad Hoc Federated Learning: A Fully Distributed Cooperative Machine Learningcited by 4
  7. 2024Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocationcited by 3
  8. 2022Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directionscited by 2
  9. 2023Collaborative Learning with a Drone Orchestratorcited by 2
  10. 2024Wireless Federated Learning over UAV-enabled Integrated Sensing and Communicationcited by 2
  11. 2023Wirelessly Powered Federated Learning Networks: Joint Power Transfer, Data Sensing, Model Training, and Resource Allocationcited by 1
  12. 2021Multi-task Federated Edge Learning (MtFEEL) in Wireless Networkscited by 0
3 more
  1. 2021Modeling and Analysis of Intermittent Federated Learning Over Cellular-Connected UAV Networkscited by 0
  2. 2023Decentralized federated learning methods for reducing communication cost and energy consumption in UAV networkscited by 0
  3. 2025Latency-aware Multimodal Federated Learning over UAV Networkscited by 0