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Federated Reinforcement Learning UAV Trajectory Design for Fast Localization of Ground Users
Open accessAbstract
In this paper, we study the localization of ground users by utilizing unmanned aerial vehicles (UAVs) as aerial anchors. Specifically, we introduce a novel localization framework based on Federated Learning (FL) and Reinforcement Learning (RL). In contrast to the existing literature, our scenario includes multiple UAVs learning the trajectory in different environment settings which results in faster convergence of RL model for minimum localization error. Furthermore, to evaluate the learned trajectory from the aggregated model, we test the trained RL agent in an alternative environment which shows the improvement over the localization error and convergence speed. Simulation results show that our proposed framework outperforms a model trained with transfer learning by %30.
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FL techniques have been used to aggregate models from edge devices, optimizing localized path predictions and reducing localization errors [124].
“FL techniques have also been used to aggregate models from edge devices, optimizing localized path predictions and reducing localization errors [124].”
From Privacy-preserving Machine Learning in Internet of Vehicle Applications: Fundamentals, Recent Advances, and Future Direction (2025) · cites this paper as [124]
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Research works printed but not linked: 7
- 10.1109/MCOM.001.1900461 · DOI · Paper page
- 10.1109/TMC.2020.2966989 · DOI
- 10.1109/MWC.2018.1800155 · DOI
- 10.1109/LWC.2014.2342736 · DOI
- 10.1109/GLOCOM.2014.7037248 · DOI
- 10.1109/JIOT.2018.2812300 · DOI
- dammann, 2013, Where2 location aided communications, European Wireless 2013 19th European Wireless Conference, 1
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Islam, N., & Zulkernine, M. (2025) · arXiv · doi:10.48550/arxiv.2503.01089 · Cited by 9
“Additionally, managing cumulative privacy loss over multiple queries or iterations (the privacy budget) is crucial to maintaining robust privacy guarantees over time.”
Metadata
- Authors
- Shahbazi, A., Donevski, I., Nielsen, J. J., & Di Renzo, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Aalborg University (DK)
- University of Paris-Saclay, CNRS, CentraleSupélec,Laboratoire des Signaux et Systèmes,Gif-Sur-Yvette,France
- Venue
- 2022 30th European Signal Processing Conference (EUSIPCO)
- Publisher
- IEEE
- Published
- 2022-08-29
- Pages
- 663-666
- OpenAlex
- W4312037985
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 1 · 2024: 6 · 2023: 4
- FWCI
- 4.38
- Type
- conference paper
- Language
- en
- Open access
- green
- vbn.aau.dk
- Locations
- European Signal Processing Conference (EUSIPCO) (publishedVersion)
- VBN Forskningsportal (Aalborg Universitet) (publishedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- HAL (submittedVersion, other-oa)
- Zenodo (CERN European Organization for Nuclear Research) (cc-by)
- Funders
- Horizon 2020 Framework Programme (812991)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › UAV Applications and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- federated learning, reinforcement learning, UAV trajectory design, multi-UAV systems, localization error minimization, convergence rate, transfer learning
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