mlsanityData to 7 Oct 2026

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

arXiv 2011.14197 ↗First posted 28 Nov 2020eess.SP cs.AIcited by 26

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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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Nov 2020.

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Citing papers per year: 2021: 1; 2022: 5; 2023: 7; 2024: 4; 2025: 7; 2026: 2.

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  1. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  2. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  3. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  4. 2022Decentral and Incentivized Federated Learning Frameworks: A Systematic Literature Reviewcited by 15
  5. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  6. 2023Over-the-Air Federated Learning and Optimizationcited by 10
  7. 2023Vertical Federated Learning over Cloud-RAN: Convergence Analysis and System Optimizationcited by 7
  8. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  9. 2022CFLIT: Coexisting Federated Learning and Information Transfercited by 4
  10. 2023Gradient Sparsification for Efficient Wireless Federated Learning with Differential Privacycited by 3
  11. 2023Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learningcited by 3
  12. 2023Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networkscited by 2
14 more
  1. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  2. 2024Wireless Federated Learning over UAV-enabled Integrated Sensing and Communicationcited by 2
  3. 2025Adaptive UAV-Assisted Hierarchical Federated Learning: Optimizing Energy, Latency, and Resilience for Dynamic Smart IoTcited by 2
  4. 2023Federated Learning in UAV-Enhanced Networks: Joint Coverage and Convergence Time Optimizationcited by 1
  5. 2025UAV-Assisted Multi-Task Federated Learning with Task Knowledge Sharingcited by 1
  6. 20225G Network on Wings: A Deep Reinforcement Learning Approach to the UAV-based Integrated Access and Backhaulcited by 0
  7. 2022VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoTcited by 0
  8. 2024Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Thingscited by 0
  9. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  10. 2025Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharingcited by 0
  11. 2025Secure UAV-assisted Federated Learning: A Digital Twin-Driven Approach with Zero-Knowledge Proofscited by 0
  12. 2025Latency-aware Multimodal Federated Learning over UAV Networkscited by 0
  13. 2026Personalized Federated Learning by Energy-Efficient UAV Communicationscited by 0
  14. 2026When Agentic AI Meets Integrated Sensing and Communicationcited by 0