# Privacy-Preserving Federated Learning for UAV-Enabled Networks: Learning-Based Joint Scheduling and Resource Management

arXiv: https://arxiv.org/abs/2011.14197 · HTML: https://mlsanity.com/paper/2011.14197/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Review](https://mlsanity.com/paper/2205.07855/) (2022)
- [Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing](https://mlsanity.com/paper/2203.09670/) (2022)
- [Over-the-Air Federated Learning and Optimization](https://mlsanity.com/paper/2310.10089/) (2023)
- [Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2305.06279/) (2023)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [CFLIT: Coexisting Federated Learning and Information Transfer](https://mlsanity.com/paper/2207.12884/) (2022)
- [Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacy](https://mlsanity.com/paper/2304.04164/) (2023)
- [Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning](https://mlsanity.com/paper/2306.02990/) (2023)
- [Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks](https://mlsanity.com/paper/2308.08792/) (2023)
- [UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysis](https://mlsanity.com/paper/2407.07739/) (2024)
- [Wireless Federated Learning over UAV-enabled Integrated Sensing and Communication](https://mlsanity.com/paper/2411.08918/) (2024)
- [Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoT](https://mlsanity.com/paper/2503.06145/) (2025)
- [Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimization](https://mlsanity.com/paper/2308.16889/) (2023)
- [UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharing](https://mlsanity.com/paper/2501.10644/) (2025)
- [5G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaul](https://mlsanity.com/paper/2202.02006/) (2022)
- [VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT](https://mlsanity.com/paper/2210.08132/) (2022)
- [Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Things](https://mlsanity.com/paper/2407.00814/) (2024)
- [HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning](https://mlsanity.com/paper/2501.09934/) (2025)
- [Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharing](https://mlsanity.com/paper/2503.09144/) (2025)
- [Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofs](https://mlsanity.com/paper/2509.13634/) (2025)
- [Latency-aware Multimodal Federated Learning over UAV Networks](https://mlsanity.com/paper/2510.01717/) (2025)
- [Personalized Federated Learning by Energy-Efficient UAV Communications](https://mlsanity.com/paper/2605.25212/) (2026)
- [When Agentic AI Meets Integrated Sensing and Communication](https://mlsanity.com/paper/2608.05792/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are capable of serving as flying base stations (BSs) for supporting data collection, artificial intelligence (AI) model training, and wireless communications. However, due to the privacy concerns of devices and limited computation or communication resource of UAVs, it is impractical to send raw data of devices to UAV servers for model training. Moreover, due to the dynamic channel condition and heterogeneous computing capacity of devices in UAV-enabled networks, the reliability and efficiency of data sharing require to be further improved. In this paper, we develop an asynchronous federated learning (AFL) framework for multi-UAV-enabled networks, which can provide asynchronous distributed computing by enabling model training locally without transmitting raw sensitive data to UAV servers. The device selection strategy is also introduced into the AFL framework to keep the low-quality devices from affecting the learning efficiency and accuracy. Moreover, we propose an asynchronous advantage actor-critic (A3C) based joint device selection, UAVs placement, and resource management algorithm to enhance the federated convergence speed and accuracy. Simulation results demonstrate that our proposed framework and algorithm achieve higher learning accuracy and faster federated execution time compared to other existing solutions.