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

arXiv: https://arxiv.org/abs/2002.08196 · HTML: https://mlsanity.com/paper/2002.08196/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2020.

## Built on by (most cited first, 30 of 31)

- [Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities](https://mlsanity.com/paper/2104.07557/) (2021)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Edge Federated Learning Via Unit-Modulus Over-The-Air Computation](https://mlsanity.com/paper/2101.12051/) (2021)
- [Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems](https://mlsanity.com/paper/2309.11039/) (2023)
- [Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges](https://mlsanity.com/paper/2111.07392/) (2021)
- [Communication-Efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data](https://mlsanity.com/paper/2107.06548/) (2021)
- [Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling](https://mlsanity.com/paper/2302.13447/) (2023)
- [EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization](https://mlsanity.com/paper/2202.10935/) (2022)
- [A Hybrid Architecture for Federated and Centralized Learning](https://mlsanity.com/paper/2105.03288/) (2021)
- [On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator](https://mlsanity.com/paper/2108.02712/) (2021)
- [Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things](https://mlsanity.com/paper/2402.02506/) (2024)
- [Federated Learning for UAV Swarms Under Class Imbalance and Power Consumption Constraints](https://mlsanity.com/paper/2108.10748/) (2021)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading](https://mlsanity.com/paper/2312.15361/) (2023)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Machine Learning Methods for Management UAV Flocks -- a Survey](https://mlsanity.com/paper/2108.13448/) (2021)
- [UAV-Aided Multi-Community Federated Learning](https://mlsanity.com/paper/2206.02043/) (2022)
- [Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization](https://mlsanity.com/paper/2308.16889/) (2023)
- [User Assignment and Resource Allocation for Hierarchical Federated Learning over Wireless Networks](https://mlsanity.com/paper/2309.09253/) (2023)
- [Federated Learning on Edge Sensing Devices: A Review](https://mlsanity.com/paper/2311.01201/) (2023)
- [A Reinforcement Learning Environment for Multi-Service UAV-enabled Wireless Systems](https://mlsanity.com/paper/2105.05094/) (2021)
- [Wireless-Enabled Asynchronous Federated Fourier Neural Network for Turbulence Prediction in Urban Air Mobility (UAM)](https://mlsanity.com/paper/2201.00626/) (2021)
- [Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks](https://mlsanity.com/paper/2202.12683/) (2022)
- [Federated Channel Learning for Intelligent Reflecting Surfaces With Fewer Pilot Signals](https://mlsanity.com/paper/2205.03196/) (2022)
- [Distributed Learning for UAV Swarms](https://mlsanity.com/paper/2410.15882/) (2024)
- [Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles](https://mlsanity.com/paper/2506.02972/) (2025)
- [MetaFed: Advancing Privacy, Performance, and Sustainability in Federated Metaverse Systems](https://mlsanity.com/paper/2508.17341/) (2025)

## 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.