# Latency Minimization for UAV-Enabled Federated Learning: Trajectory Design and Resource Allocation

arXiv: https://arxiv.org/abs/2412.07428 · HTML: https://mlsanity.com/paper/2412.07428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 25 of 25)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (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)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020)
- [Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks](https://mlsanity.com/paper/1810.10408/) (2018)
- [Optimized Power Control Design for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2106.09316/) (2021)
- [Accelerating DNN Training in Wireless Federated Edge Learning Systems](https://mlsanity.com/paper/1905.09712/) (2019)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence](https://mlsanity.com/paper/2010.09317/) (2020)
- [Joint Device Selection and Power Control for Wireless Federated Learning](https://mlsanity.com/paper/2205.09306/) (2022)
- [UAV Communications for Sustainable Federated Learning](https://mlsanity.com/paper/2103.11073/) (2021)
- [Joint Auction-Coalition Formation Framework for Communication-Efficient Federated Learning in UAV-Enabled Internet of Vehicles](https://mlsanity.com/paper/2007.06378/) (2020)
- [Federated Learning via Unmanned Aerial Vehicle](https://mlsanity.com/paper/2210.10970/) (2022)
- [Efficient Wireless Federated Learning with Partial Model Aggregation](https://mlsanity.com/paper/2204.09746/) (2022)
- [UAV-Enabled Wireless Power Transfer: A Tutorial Overview](https://mlsanity.com/paper/2103.00207/) (2021)
- [Joint Speed Control and Energy Replenishment Optimization for UAV-assisted IoT Data Collection with Deep Reinforcement Transfer Learning](https://mlsanity.com/paper/2106.10423/) (2021)
- [Collaborative Learning with a Drone Orchestrator](https://mlsanity.com/paper/2303.02266/) (2023)
- [Real-Time Resource Allocation for Wireless Powered Multiuser Mobile Edge Computing With Energy and Task Causality](https://mlsanity.com/paper/2004.14319/) (2020)
- [Networked ISAC for Low-Altitude Economy: Transmit Beamforming and UAV Trajectory Design](https://mlsanity.com/paper/2405.07568/) (2024)
- [UAV-Enabled Wireless Networks with Movable-Antenna Array: Flexible Beamforming and Trajectory Design](https://mlsanity.com/paper/2405.20746/) (2024)
- [Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy](https://mlsanity.com/paper/2406.07374/) (2024)

## Built on by (most cited first, 3 of 3)

- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [Computation- and Communication-Efficient Online FL for Resource-Constrained Aerial Vehicles](https://mlsanity.com/paper/2506.02972/) (2025)
- [Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofs](https://mlsanity.com/paper/2509.13634/) (2025)

## Abstract (arXiv)

Federated learning (FL) has become a transformative paradigm for distributed machine learning across wireless networks. However, the performance of FL is often hindered by the unreliable communication links between resource-constrained Internet of Things (IoT) devices and the central server. To overcome this challenge, we propose a novel framework that employs an unmanned aerial vehicle (UAV) as a mobile server to enhance the FL training process. By capitalizing on the UAV's mobility, we establish strong line-of-sight connections with IoT devices, thereby enhancing communication reliability and capacity. To maximize training efficiency, we formulate a latency minimization problem that jointly optimizes bandwidth allocation, computing frequencies, transmit power for both the UAV and IoT devices, and the UAV's flight trajectory. Subsequently, we analyze the required rounds of the IoT devices training and the UAV aggregation for FL convergence. Based on the convergence constraint, we transform the problem into three subproblems and develop an efficient alternating optimization algorithm to solve this problem effectively. Additionally, we provide a thorough analysis of the algorithm's convergence and computational complexity. Extensive numerical results demonstrate that our proposed scheme not only surpasses existing benchmark schemes in reducing latency up to 15.29%, but also achieves training efficiency that nearly matches the ideal scenario.