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المؤلفMrad, Ilyes
المؤلفSamara, Lutfi
المؤلفAbdellatif, Alaa Awad
المؤلفAl-Abbasi, Abubakr
المؤلفHamila, Ridha
المؤلفErbad, Aiman
تاريخ الإتاحة2023-04-04T09:09:09Z
تاريخ النشر2021
اسم المنشور2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/GLOBECOM46510.2021.9685143
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41645
الملخصThe usage of unmanned aerial vehicles (UAVs) in civil and military applications continues to increase due to the numerous advantages that they provide over conventional approaches. Despite the abundance of such advantages, it is imperative to investigate the performance of UAV utilization while considering their design limitations. This paper investigates the deployment of UAV swarms when each UAV carries a machine learning classification task. To avoid data exchange with ground-based processing nodes, a federated learning approach is adopted between a UAV leader and the swarm members to improve the local learning model while avoiding excessive air-to-ground and ground-to-air communications. Moreover, the proposed de-ployment framework considers the stringent energy constraints of UAVs and the problem of class imbalance, where we show that considering these design parameters significantly improves the performances of the UAV swarm in terms of classification accuracy, energy consumption and availability of UAVs when compared with several baseline algorithms. 2021 IEEE.
راعي المشروعVI. ACKNOWLEDGEMENT This paper was made possible by PDRA grant #5-0424-19005 from the Qatar National Research Fund (a member of Qatar Foundation) and the Qatar University Internal Grant IRCC-2020-001. The statements made herein are solely the responsibility of the authors.
اللغةen
الناشرIEEE
الموضوعClass Imbalance
Federated Learning
UAV Swarm
العنوانFederated Learning for UAV Swarms under Class Imbalance and Power Consumption Constraints
النوعConference Paper
dc.accessType Abstract Only


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