Client selection for federated edge learning in UAV networks / Ilyes Akram Ahmed Chaouch

Public ISBD Titre : Client selection for federated edge learning in UAV networks Type de document : document multimédia Auteurs : Ilyes Akram Ahmed Chaouch, Auteur ; Mohamed Khaled Ali Oudnani, Auteur ; Yousra Cheriguene, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 41 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Networks, distributed systems and applications Langues : Anglais Mots-clés : FL  UAV Networks  Client selection  Mobility-Aware selection  Edge intelligence Résumé : One promising way to enable distributed intelligence at the edge while protecting data privacy is to integrate Federated Learning (FL) with Unmanned Aerial Vehicles (UAV) networks.Using FL enables each UAV to cooperatively train a global model without sharing raw data, especially in UAV swarms used for surveillance, monitoring, or emergency response missions. But choosing the best clients (UAVs) for every training cycle is made extremely difficult by the dynamic and diverse character of UAV environments. These difficulties are brought on by things like fluctuating connectivity, shifting patterns of movement, and energy limitations. In this work, we investigate the problem of client selection for Federated Edge Learning in UAV networks. We first present a taxonomy of existing selection strategies, considering criteria such as model performance and UAV mobility. Then, we propose an adaptive client selection framework that integrates both mobility-awareness and distance with speed to enhance learning efficiency and model accuracy. Extensive simulations demonstrate that our method significantly improves convergence speed and reduces communication overhead, while maintaining high model performance in dynamic UAV scenarios. note de thèses : Mémoire de master en informatique Client selection for federated edge learning in UAV networks [document multimédia] / Ilyes Akram Ahmed Chaouch, Auteur ; Mohamed Khaled Ali Oudnani, Auteur ; Yousra Cheriguene, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 41 p. + 1 disque optique numérique (CD-ROM). Option : Networks, distributed systems and applications Langues : Anglais Mots-clés : FL  UAV Networks  Client selection  Mobility-Aware selection  Edge intelligence Résumé : One promising way to enable distributed intelligence at the edge while protecting data privacy is to integrate Federated Learning (FL) with Unmanned Aerial Vehicles (UAV) networks.Using FL enables each UAV to cooperatively train a global model without sharing raw data, especially in UAV swarms used for surveillance, monitoring, or emergency response missions. But choosing the best clients (UAVs) for every training cycle is made extremely difficult by the dynamic and diverse character of UAV environments. These difficulties are brought on by things like fluctuating connectivity, shifting patterns of movement, and energy limitations. In this work, we investigate the problem of client selection for Federated Edge Learning in UAV networks. We first present a taxonomy of existing selection strategies, considering criteria such as model performance and UAV mobility. Then, we propose an adaptive client selection framework that integrates both mobility-awareness and distance with speed to enhance learning efficiency and model accuracy. Extensive simulations demonstrate that our method significantly improves convergence speed and reduces communication overhead, while maintaining high model performance in dynamic UAV scenarios. note de thèses : Mémoire de master en informatique

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