Federated Learning for UAV Swarms under Class Imbalance and Power Consumption Constraints
المؤلف | Mrad, Ilyes |
المؤلف | Samara, Lutfi |
المؤلف | Abdellatif, Alaa Awad |
المؤلف | Al-Abbasi, Abubakr |
المؤلف | Hamila, Ridha |
المؤلف | Erbad, Aiman |
تاريخ الإتاحة | 2023-04-04T09:09:09Z |
تاريخ النشر | 2021 |
اسم المنشور | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings |
المصدر | Scopus |
الملخص | The 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 المشروع | VI. 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. |
اللغة | en |
الناشر | IEEE |
الموضوع | Class Imbalance Federated Learning UAV Swarm |
النوع | Conference Paper |
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