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

Contribution to the UAV-based mobile edge computing communication strategies / Yousra Cheriguene

Public ISBD Titre : Contribution to the UAV-based mobile edge computing communication strategies Type de document : document multimédia Auteurs : Yousra Cheriguene, Auteur ; Chaker Abdelaziz Kerrache, Directeur de thèse ; Fatima Zahra Bousbaa, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2024 Importance : 120 p. Langues : Anglais Mots-clés : Unmanned aerial vehicle  UAV  Federated learning  FL  Edge computing  MEC Résumé : In recent years, the surge in data from smart devices and the Internet of Things (IoT) has propelled the need for efficient data storage, transportation, and analysis. This has led to a groundbreaking era in wireless communication and computing frameworks with the integration of Unmanned Aerial Vehicles (UAVs) in Mobile Edge Computing (MEC). This thesis explores UAV-assisted MEC, highlighting opportunities and addressing key challenges, specifically focusing on implementing Federated Learning (FL). FL, a decentralized machine learning (ML) method, allows users to collectively train an ML model without revealing their private local datasets. The thesis first optimizes UAV participant selection for edge FL, enhancing accuracy by considering factors like energy consumption, communication quality, and local dataset diversity. Empirical studies demonstrate the superior performance of the proposed selection scheme over the random selection benchmark. The second contribution introduces a novel client selection method improving convergence by prioritizing UAVs with high reliability and excluding malicious UAVs. The strategy outperforms baseline methods, as verified by evaluations under diverse attack scenarios. The third contribution presents an energy-efficient inter-UAV multicast routing protocol, COCOMA, and its enhancement, COCOMA+. Extensive simulations confirm their efficacy in establishing an efficient communication backbone with Quality-of-Service (QoS) attributes and reducing total emission energy. The thesis concludes by outlining open challenges and initiating discussions on future research directions in UAV-assisted MEC, fostering continued advancements and innovation. note de thèses : Thése de doctorat en informatique Contribution to the UAV-based mobile edge computing communication strategies [document multimédia] / Yousra Cheriguene, Auteur ; Chaker Abdelaziz Kerrache, Directeur de thèse ; Fatima Zahra Bousbaa, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2024 . - 120 p. Langues : Anglais Mots-clés : Unmanned aerial vehicle  UAV  Federated learning  FL  Edge computing  MEC Résumé : In recent years, the surge in data from smart devices and the Internet of Things (IoT) has propelled the need for efficient data storage, transportation, and analysis. This has led to a groundbreaking era in wireless communication and computing frameworks with the integration of Unmanned Aerial Vehicles (UAVs) in Mobile Edge Computing (MEC). This thesis explores UAV-assisted MEC, highlighting opportunities and addressing key challenges, specifically focusing on implementing Federated Learning (FL). FL, a decentralized machine learning (ML) method, allows users to collectively train an ML model without revealing their private local datasets. The thesis first optimizes UAV participant selection for edge FL, enhancing accuracy by considering factors like energy consumption, communication quality, and local dataset diversity. Empirical studies demonstrate the superior performance of the proposed selection scheme over the random selection benchmark. The second contribution introduces a novel client selection method improving convergence by prioritizing UAVs with high reliability and excluding malicious UAVs. The strategy outperforms baseline methods, as verified by evaluations under diverse attack scenarios. The third contribution presents an energy-efficient inter-UAV multicast routing protocol, COCOMA, and its enhancement, COCOMA+. Extensive simulations confirm their efficacy in establishing an efficient communication backbone with Quality-of-Service (QoS) attributes and reducing total emission energy. The thesis concludes by outlining open challenges and initiating discussions on future research directions in UAV-assisted MEC, fostering continued advancements and innovation. note de thèses : Thése de doctorat en informatique

Detection of large scale influence operations using machine learning techniques / Yasser Hacini

Public ISBD Titre : Detection of large scale influence operations using machine learning techniques Type de document : document multimédia Auteurs : Yasser Hacini, Auteur ; Yousra Cheriguene, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2025 Importance : 32 p. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Data science and artificial intelligence Langues : Anglais Mots-clés : Artificial intelligence  Propaganda  Word embedding  WEAT Résumé : With the ever-increasing popularity of social media, and the rising prevalence of fifth generation warfare that relies heavily on non-kinetic techniques such as propaganda, social engineering, hacking, etc. There has been a significant increase of large scale influence operation, which has become significantly easier to carry out with the recent advance- ments in generative AI and large language models, this has increased prejudice between communities, which has in turn decreased the tolerance seen between each other. In this dissertation we propose a technique which allows for the enumeration of both explicit and implicit biases found in one or multiple communities using a technique known as WEAT (Word embedding association tests), we mainly use it to find problematic associations made by these communities and how these associations change over time in response to outside influence. Using our technique, we were able to achieve an average P value of 0.033 and have been able to show clear problematic associations made by different communi- ties, we also detected sudden shifts in associations which correlated with related outside events note de thèses : Mémoire de master en informatique Detection of large scale influence operations using machine learning techniques [document multimédia] / Yasser Hacini, Auteur ; Yousra Cheriguene, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2025 . - 32 p. + 1 disque optique numérique (CD-ROM). Option : Data science and artificial intelligence Langues : Anglais Mots-clés : Artificial intelligence  Propaganda  Word embedding  WEAT Résumé : With the ever-increasing popularity of social media, and the rising prevalence of fifth generation warfare that relies heavily on non-kinetic techniques such as propaganda, social engineering, hacking, etc. There has been a significant increase of large scale influence operation, which has become significantly easier to carry out with the recent advance- ments in generative AI and large language models, this has increased prejudice between communities, which has in turn decreased the tolerance seen between each other. In this dissertation we propose a technique which allows for the enumeration of both explicit and implicit biases found in one or multiple communities using a technique known as WEAT (Word embedding association tests), we mainly use it to find problematic associations made by these communities and how these associations change over time in response to outside influence. Using our technique, we were able to achieve an average P value of 0.033 and have been able to show clear problematic associations made by different communi- ties, we also detected sudden shifts in associations which correlated with related outside events note de thèses : Mémoire de master en informatique

Multicast routing in swarm of UAVs / Soumia Djellikh

Public ISBD Titre : Multicast routing in swarm of UAVs Type de document : texte manuscrit Auteurs : Soumia Djellikh, Auteur ; Yousra Cheriguene, Auteur ; Fatima Zahra Bousbaa, Directeur de thèse Editeur : Laghouat : Université Amar Telidji - Département d'informatique Année de publication : 2020 Importance : 55 p. Format : 30 cm. Accompagnement : 1 disque optique numérique (CD-ROM) Note générale : Option : Systems and computer science networks Langues : Anglais Mots-clés : UAVS  Swarm of UAVS  Multicast Routing Protocol  SEMRP-V1  SEMRP- V2 Résumé : Recently, the deployment of a swarm of cooperative Unmanned Aerial Vehicles (UAVS) to pursue a task is enjoying increasing success, since a group of UAVS instead of one single UAV leads to many advantages, for example the possibility to extend the mission coverage, to guarantee a reliable ad-hoc network, or to enhance the service performance. However, it also poses many challenges in designing networking protocols such as, the dynamic topology change. In this work, we study the multicast routing problem in swarm of UAVS, which aims at delivering information to specific members of flying nodes. Our objective is to provide detailed classification of existing swarm routing protocols considering transmission strategies and to propose a new Energy eflicient Multicast Routing Protocol for UAVS Swarm (SEMRP) taking into consideration the aforementioned challenges and aimning to satisfy COronaVirus Disease 2019 (COVID-19) applications. The results of the simulation conducted using NS-2 simulator advocate for the efficiency of our method through two proposed versions (SEMRP-V1 and SEMRP-V2) in term of reducing the total emission energy (at least by 10 dBm), optimizing the End-to-End Delay by 44%, and increasing the packet delivery ratio by more than to 22% compared to SP-GMRF protocol. note de thèses : Mémoire de master en informatique Multicast routing in swarm of UAVs [texte manuscrit] / Soumia Djellikh, Auteur ; Yousra Cheriguene, Auteur ; Fatima Zahra Bousbaa, Directeur de thèse . - Laghouat : Université Amar Telidji - Département d'informatique, 2020 . - 55 p. ; 30 cm. + 1 disque optique numérique (CD-ROM). Option : Systems and computer science networks Langues : Anglais Mots-clés : UAVS  Swarm of UAVS  Multicast Routing Protocol  SEMRP-V1  SEMRP- V2 Résumé : Recently, the deployment of a swarm of cooperative Unmanned Aerial Vehicles (UAVS) to pursue a task is enjoying increasing success, since a group of UAVS instead of one single UAV leads to many advantages, for example the possibility to extend the mission coverage, to guarantee a reliable ad-hoc network, or to enhance the service performance. However, it also poses many challenges in designing networking protocols such as, the dynamic topology change. In this work, we study the multicast routing problem in swarm of UAVS, which aims at delivering information to specific members of flying nodes. Our objective is to provide detailed classification of existing swarm routing protocols considering transmission strategies and to propose a new Energy eflicient Multicast Routing Protocol for UAVS Swarm (SEMRP) taking into consideration the aforementioned challenges and aimning to satisfy COronaVirus Disease 2019 (COVID-19) applications. The results of the simulation conducted using NS-2 simulator advocate for the efficiency of our method through two proposed versions (SEMRP-V1 and SEMRP-V2) in term of reducing the total emission energy (at least by 10 dBm), optimizing the End-to-End Delay by 44%, and increasing the packet delivery ratio by more than to 22% compared to SP-GMRF protocol. note de thèses : Mémoire de master en informatique

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