mlsanityData to 7 Oct 2026

Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms

arXiv 2002.08196 ↗First posted 19 Feb 2020cs.LG cs.IT cs.ROcited by 31

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Abstract (arXiv)

Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks ranging from coordinated trajectory planning to cooperative target recognition. However, due to the lack of continuous connections between the UAV swarm and ground base stations (BSs), using centralized ML will be challenging, particularly when dealing with a large volume of data. In this paper, a novel framework is proposed to implement distributed federated learning (FL) algorithms within a UAV swarm that consists of a leading UAV and several following UAVs. Each following UAV trains a local FL model based on its collected data and then sends this trained local model to the leading UAV who will aggregate the received models, generate a global FL model, and transmit it to followers over the intra-swarm network. To identify how wireless factors, like fading, transmission delay, and UAV antenna angle deviations resulting from wind and mechanical vibrations, impact the performance of FL, a rigorous convergence analysis for FL is performed. Then, a joint power allocation and scheduling design is proposed to optimize the convergence rate of FL while taking into account the energy consumption during convergence and the delay requirement imposed by the swarm's control system. Simulation results validate the effectiveness of the FL convergence analysis and show that the joint design strategy can reduce the number of communication rounds needed for convergence by as much as 35% compared with the baseline design.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Feb 2020.

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Citing papers per year: 2021: 12; 2022: 5; 2023: 9; 2024: 2; 2025: 3.

31 papers.

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  1. 2021Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunitiescited by 22
  2. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  3. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  4. 2021Edge Federated Learning Via Unit-Modulus Over-The-Air Computationcited by 16
  5. 2023Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problemscited by 15
  6. 2021Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challengescited by 12
  7. 2021Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Datacited by 10
  8. 2023Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Schedulingcited by 10
  9. 2021A Hybrid Architecture for Federated and Centralized Learningcited by 8
  10. 2022EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalizationcited by 8
  11. 2021On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestratorcited by 6
  12. 2024Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Thingscited by 6
19 more
  1. 2021Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraintscited by 5
  2. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  3. 2023Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semanticscited by 5
  4. 2023Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloadingcited by 5
  5. 2023Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insightscited by 4
  6. 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learningcited by 3
  7. 2021Machine Learning Methods for Management UAV Flocks -- a Surveycited by 2
  8. 2022UAV-Aided Multi-Community Federated Learningcited by 1
  9. 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimizationcited by 1
  10. 2023User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networkscited by 1
  11. 2023Federated Learning on Edge Sensing Devices: A Reviewcited by 1
  12. 2021A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systemscited by 0
  13. 2021Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)cited by 0
  14. 2022Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networkscited by 0
  15. 2022Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signalscited by 0
  16. 2024Distributed Learning for UAV Swarmscited by 0
  17. 2025Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehiclescited by 0
  18. 2025MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systemscited by 0
  19. 2025Latency-aware Multimodal Federated Learning over UAV Networkscited by 0