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Tesi sul tema "Federated network"

Autore: Grafiati

Pubblicato: 25 gennaio 2025

Ultima modifica: 31 luglio 2025

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1

Kulkarni, Shweta Samir. "SECURE MIDDLEWARE FOR FEDERATED NETWORK PERFORMANCE MONITORING." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366333088.

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2

Maka, Stephan. "Design and Implementation of a Federated Social Network." Master's thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-75477.

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3

Felt, Aaron J. "Federated ground station network model and interface specification." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44558.

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Approved for public release; distribution is unlimited<br>This thesis solves the problem of a lack of a complete, simple ground station network interface standard. A federated satellite ground station network (FGN) model and computer interface are developed that extend the use of ground stations to external users across the Internet. This should allow for reuse of existing ground stations, reducing costs and complexity of space missions. An improved model describing FGNs is proposed that defines a hierarchy of the components of the network, allowing for scalability and unified interfaces, and

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4

Demirci, Turan. "Federated Simulation Of Network Performance Using Packet Flow Modeling." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12611704/index.pdf.

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Federated approach for the distributed simulation of a network, is an alternative method that aims to combine existing simulation models and software together using a Run Time Infrastructure (RTI), rather than building the whole simulation from scratch. In this study, an approach that significantly reduces the inter-federate communication load in federated simulation of communication networks is proposed. Rather than communicating packet-level information among federates, characteristics of packet flows in individual federates are dynamically identified and communicated. Flow characterization

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5

Cetin, Burak. "Wireless Network Intrusion Detection and Analysis using Federated Learning." Youngstown State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1588778320687729.

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6

Xu, Ran. "Federated Sensor Network architectural design for the Internet of Things (IoT)." Thesis, Loughborough University, 2013. https://dspace.lboro.ac.uk/2134/13453.

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An information technology that can combine the physical world and virtual world is desired. The Internet of Things (IoT) is a concept system that uses Radio Frequency Identification (RFID), WSN and barcode scanners to sense and to detect physical objects and events. This information is shared with people on the Internet. With the announcement of the Smarter Planet concept by IBM, the problem of how to share this data was raised. However, the original design of WSN aims to provide environment monitoring and control within a small scale local network. It cannot meet the demands of the IoT becaus

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7

Lu, Zonghao. "A case study about different network architectures in Federated Machine Learning." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-425193.

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Modern artificial intelligence (AI) technology is developing rapidly in recent years. Data is an important factor driving the development of AI.With the development of mobile Internet, more and more data is generated in different fields every day along with data sensitivity issues. As asignificant part of personal privacy, personal data must be respected and protected. Federated learning (FL) is a machine learning technology that can protect privacy because it keeps everyone’s data local. Many types of research have already confirmed that the bottleneck of federated learning is the communicati

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8

Grativol, Ribeiro Lucas. "Neural network compression in the context of federated learning and edge devices." Electronic Thesis or Diss., Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2024. http://www.theses.fr/2024IMTA0444.

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L’apprentissage fédéré est un cadre d’apprentissage automatique collaboratif et décentralisé, motivé par des préoccupations croissantes concernant la confidentialité desdonnées. En transférant l’entraînement des modèles vers des noeuds locaux et en conservant les données sur place, il favorise une approche plus respectueuse de la vie privée. Toutefois, cette méthode impose un surcoût en termes de communication et de calcul à ceux qui l’adoptent. Dans ce manuscrit, nous examinons les principaux défis de l’apprentissage fédéré et proposons des solutions visant à augumenter l’efficacité tout en r

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9

Sani, Lorenzo. "Unsupervised clustering of MDS data using federated learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022. http://amslaurea.unibo.it/25591/.

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In this master thesis we developed a model for unsupervised clustering on a data set of biomedical data. This data has been collected by GenoMed4All consortium from patients affected by Myelodysplastic Syndrome (MDS), that is an haematological disease. The main focus is put on the genetic mutations collected that are used as features of the patients in order to cluster them. Clustering approaches have been used in several studies concerning haematological diseases such MDS. A neural network-based model was used to solve the task. The results of the clustering have been compared with labels fro

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10

Gopalakrishnan, Aravind. "Network and Middleware Security for Enterprise Network Monitoring." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339742304.

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11

Vikström, Johan. "Comparing decentralized learning to Federated Learning when training Deep Neural Networks under churn." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300391.

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Decentralized Machine Learning could address some problematic facets with Federated Learning. There is no central server acting as an arbiter of whom or what may benefit from Machine Learning models created by the vast amount of data becoming available in recent years. It could also increase the reliability and scalability of Machine Learning systems thereby drawing the benefit of having more data accessible. Gossip Learning is such a protocol, but has primarily been designed with linear models in mind. How does Gossip Learning perform when training Deep Neural Networks? Could it be a viable a

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12

Eriksson, Henrik. "Federated Learning in Large Scale Networks : Exploring Hierarchical Federated Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292744.

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Federated learning faces a challenge when dealing with highly heterogeneous data and it can sometimes be inadequate to adopt an approach where a single model is trained for usage at all nodes in the network. Different approaches have been investigated to succumb this issue such as adapting the trained model to each node and clustering the nodes in the network and train a different model for each cluster where the data is less heterogeneous. In this work we study the possibilities to improve the local model performance utilizing the hierarchical setup that comes with clustering the participatin

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13

Martins, Dalton Lopes. "Análise de redes sociais de colaboração científica no ambiente de uma federação de bibliotecas digitais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/27/27151/tde-16042013-144121/.

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A produção científica de uma área do conhecimento aparece em diferentes formatos e é disponibilizada de forma essencialmente distribuída por entre revistas, anais, teses, dissertações e outros formatos característicos utilizados pela comunidade científica para a sistematização de seu discurso. Uma federação de bibliotecas digitais oferece uma arquitetura da informação que tem por finalidade facilitar a agregação de diferentes tipos de documentos disponibilizados, facilitando termos acesso a esses documentos, bem como a seus metadados descritores, formando, desse modo, verdadeiras estruturas de

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14

Bhogi, Keerthana. "Two New Applications of Tensors to Machine Learning for Wireless Communications." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/104970.

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With the increasing number of wireless devices and the phenomenal amount of data that is being generated by them, there is a growing interest in the wireless communications community to complement the traditional model-driven design approaches with data-driven machine learning (ML)-based solutions. However, managing the large-scale multi-dimensional data to maintain the efficiency and scalability of the ML algorithms has obviously been a challenge. Tensors provide a useful framework to represent multi-dimensional data in an integrated manner by preserving relationships in data across different

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15

Zhao, Qiwei. "Federated Learning with Heterogeneous Challenge." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/27399.

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Federated learning allows the training of a model from the distributed data of many clients under the orchestration of a central server. With the increasing concern on privacy, federated learning draws great attention from both academia and industry. However, the heterogeneous challenges introduced by natural characters of federated learning settings significantly degrade the performance of federated learning methods. Specifically, these heterogeneous challenges include the heterogeneous data challenges and the heterogeneous scenario challenges. Data heterogeneous challenges mean the signif

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16

Lluch-Ariet, Magí. "Contributions to efficient and secure exchange of networked clinical data : the MOSAIC system." Doctoral thesis, Universitat Politècnica de Catalunya, 2016. http://hdl.handle.net/10803/388037.

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The understanding of certain data often requires the collection of similar data from different places to be analysed and interpreted. Multi-agent systems (MAS), interoperability standards (DICOM, HL7 or EN13606) and clinical Ontologies, are facilitating data interchange among different clinical centres around the world. However, as more and more data becomes available, and more heterogeneous this data gets, the task of accessing and exploiting the large number of distributed repositories to extract useful knowledge becomes increasingly complex. Beyond the existing networks and advances for

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17

JIANG, JIN. "Social distributed content caching in federated residential networks." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2506271.

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This work addresses the need for content sharing and backup in household equipped with a home gateway that stores, tags and manages the data collected by the home users. Our solution leverages the interaction between remote gateways in a social way, i.e., by exploiting the users’ social networking information, so that caching recipients are those gateways whose users are most likely to be interested in accessing the shared content. We formulate this problem as a Budgeted Maximum Coverage (BMC) problem and we numerically compute the optimal content caching solution. We then propose a low-comple

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18

Waugh, Daniel. "Oceania Football Confederation the impact of affiliate disaffiliation on the inter-organizational dynamics of a federated network : a dissertation submitted to Auckland University of Technology in partial fulfilment of the requirements for the degree of Master of Business (MBus), 2009 /." Click here to access this resource online, 2009. http://hdl.handle.net/10292/796.

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19

Ariyattu, Resmi. "Towards federated social infrastructures for plug-based decentralized social networks." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S031/document.

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Dans cette thèse, nous abordons deux problèmes soulevés par les systèmes distribués décentralisés - le placement de réseaux logiques de façon compatible avec le réseau physique sous-jacent et la construction de cohortes d'éditeurs pour dans les systèmes d'édition collaborative. Bien que les réseaux logiques (overlay networks) été largement étudiés, la plupart des systèmes existant ne prennent pas ou prennent mal en compte la topologie du réseau physique sous-jacent, alors que la performance de ces systèmes dépend dans une grande mesure de la manière dont leur topologie logique exploite la loca

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20

Backstad, Sebastian. "Federated Averaging Deep Q-NetworkA Distributed Deep Reinforcement Learning Algorithm." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149637.

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In the telecom sector, there is a huge amount of rich data generated every day. This trend will increase with the launch of 5G networks. Telco companies are interested in analyzing their data to shape and improve their core businesses. However, there can be a number of limiting factors that prevents them from logging data to central data centers for analysis. Some examples include data privacy, data transfer, network latency etc. In this work, we present a distributed Deep Reinforcement Learning (DRL) method called Federated Averaging Deep Q-Network (FADQN), that employs a distributed hierarc

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21

Basnayake, Mudiyanselage V. (Vishaka). "Federated learning for enhanced sensor reliability of automated wireless networks." Master's thesis, University of Oulu, 2019. http://jultika.oulu.fi/Record/nbnfioulu-201908142761.

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Abstract. Autonomous mobile robots working in-proximity humans and objects are becoming frequent and thus, avoiding collisions becomes important to increase the safety of the working environment. This thesis develops a mechanism to improve the reliability of sensor measurements in a mobile robot network taking into the account of inter-robot communication and costs of faulty sensor replacements. In this view, first, we develop a sensor fault prediction method utilizing sensor characteristics. Then, network-wide cost capturing sensor replacements and wireless communication is minimized subject

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22

Knight-Williams, Alex. "Time to Next Flow Classification in Mobile Networks with Federated Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287183.

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Understanding traffic dynamics and user demand in a cellular network is essential for effective resource management, which in turn improves the network’s energy and cost efficiency. This thesis focuses on the task of classifying the time until the arrival of the next flow at a user level in a real network traffic data set. A range of machine learning and deep learning techniques are applied to this task, some of which are incorporated into a federated learning framework that ensures data privacy. The results demonstrate that the long short-term memory (LSTM) performs best on this task, althoug

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23

Mestoukirdi, Mohamad. "Reliable and Communication-Efficient Federated Learning for Future Intelligent Edge Networks." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS432.

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Dans le domaine des futurs réseaux sans fil 6G, l'intégration de la périphérie intelligente grâce à l'avènement de l'IA représente un bond en avant considérable, promettant des avancées révolutionnaires en matière de communication sans fil. Cette intégration favorise une synergie harmonieuse, capitalisant sur le potentiel collectif de ces technologies transformatrices. Au cœur de cette intégration se trouve le rôle de l'apprentissage fédéré, un paradigme d'apprentissage décentralisé qui préserve la confidentialité des données tout en exploitant l'intelligence collective des appareils interconn

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24

Benikhlef, Sarah. "Federated learning of Bayesian networks preserving privacy for personalised medical applications." Electronic Thesis or Diss., Nantes Université, 2025. http://www.theses.fr/2025NANU4001.

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L’apprentissage fédéré permet d’entraîner des modèles d’apprentissage automatique sur plusieurs ensembles de données décentralisés sans partager les données brutes, répondant ainsi aux préoccupations critiques en matière de confidentialité dans le domaine de la santé. Les réseaux bayésiens (RB) sont des modèles probabilistes qui ont prouvé leur valeur dans la modélisation de dépendances complexes. Ils ont la particularité d’être grandement interprétables, ce qui est crucial lors du traitement de données médicales, car cela permet aux experts médicaux de comprendre facilement les modèles constr

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25

Díaz, González Fernando. "Federated Learning for Time Series Forecasting Using LSTM Networks: Exploiting Similarities Through Clustering." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254665.

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Federated learning poses a statistical challenge when training on highly heterogeneous sequence data. For example, time-series telecom data collected over long intervals regularly shows mixed fluctuations and patterns. These distinct distributions are an inconvenience when a node not only plans to contribute to the creation of the global model but also plans to apply it on its local dataset. In this scenario, adopting a one-fits-all approach might be inadequate, even when using state-of-the-art machine learning techniques for time series forecasting, such as Long Short-Term Memory (LSTM) netwo

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26

Langelaar, Johannes, and Mattsson Adam Strömme. "Federated Neural Collaborative Filtering for privacy-preserving recommender systems." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446913.

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In this thesis a number of models for recommender systems are explored, all using collaborative filtering to produce their recommendations. Extra focus is put on two models: Matrix Factorization, which is a linear model and Multi-Layer Perceptron, which is a non-linear model. With an additional purpose of training the models without collecting any sensitive data from the users, both models were implemented with a learning technique that does not require the server's knowledge of the users' data, called federated learning. The federated version of Matrix Factorization is already well-researched

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Li, Yuntao. "Federated Learning for Time Series Forecasting Using Hybrid Model." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254677.

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Time Series data has become ubiquitous thanks to affordable edge devices and sensors. Much of this data is valuable for decision making. In order to use these data for the forecasting task, the conventional centralized approach has shown deficiencies regarding large data communication and data privacy issues. Furthermore, Neural Network models cannot make use of the extra information from the time series, thus they usually fail to provide time series specific results. Both issues expose a challenge to large-scale Time Series Forecasting with Neural Network models. All these limitations lead to

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28

Leconte, Louis. "Compression and federated learning : an approach to frugal machine learning." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS107.

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Les appareils et outils “intelligents” deviennent progressivement la norme, la mise en œuvre d'algorithmes basés sur des réseaux neuronaux artificiels se développant largement. Les réseaux neuronaux sont des modèles non linéaires d'apprentissage automatique avec de nombreux paramètres qui manipulent des objets de haute dimension et obtiennent des performances de pointe dans divers domaines, tels que la reconnaissance d'images, la reconnaissance vocale, le traitement du langage naturel et les systèmes de recommandation.Toutefois, l'entraînement d'un réseau neuronal sur un appareil à faible capa

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29

Hark, Rhaban Simon [Verfasser], Ralf [Akademischer Betreuer] Steinmetz, and Andreas [Akademischer Betreuer] Mauthe. "Monitoring Federated Softwarized Networks: Approaches for Efficient and Collaborative Data Collection in Large-Scale Software-Defined Networks / Rhaban Simon Hark ; Ralf Steinmetz, Andreas Mauthe." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2019. http://d-nb.info/1196792550/34.

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30

Ricco, Christophe. "Soluzione Android-based per il Discovery e l'Accesso a Servizi fra Reti Locali Federate." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019.

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Negli ultimi anni abbiamo assistito alla rapida proliferazione di dispositivi sempre più piccoli in termini di dimensioni ma con delle capacità computazionali sempre più elevate. Con il numero di questi dispositivi in continua crescita, unita alle tecnologie di comunicazione sempre più economiche è nata quella che viene chiamata Internet of Things. La caratteristica principale per far parte di Internet of Things è quella di essere in grado di comunicare con gli altri. Ad oggi le maggiore tecnologie utilizzate sono Wifi e LTE ma necessitano di una infrastruttura esistente sul territorio. Al fin

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31

Subramanya, Tejas. "Autonomic Management and Orchestration Strategies in MEC-Enabled 5G Networks." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/320883.

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5G and beyond mobile network technology promises to deliver unprecedented ultra-low latency and high data rates, paving the way for many novel applications and services. Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC) are two technologies expected to play a vital role in achieving ambitious Quality of Service requirements of such applications. While NFV provides flexibility by enabling network functions to be dynamically deployed and inter-connected to realize Service Function Chains (SFC), MEC brings the computing capability to the mobile network's edges, thus redu

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32

Griffier, Romain. "Intégration et utilisation secondaire des données de santé hospitalières hétérogènes : des usages locaux à l'analyse fédérée." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0479.

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Les données issues du soin peuvent être utilisées pour des finalités autres que celles pour lesquelles elles ont été collectées initialement : c’est l’utilisation secondaire des données de santé. Dans le contexte hospitalier, afin de lever les verrous de l’utilisation secondaire des données de santé (verrous liés aux données et verrous organisationnels), une stratégie classique consiste à mettre en place un Entrepôt de Données de Santé (EDS). Dans le cadre de cette thèse, trois contributions à l’EDS du CHU de Bordeaux sont décrites. Premièrement, une méthode d’alignement des data éléments de b

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Chafaa, Irched. "Machine learning for beam alignment in mmWave networks." Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG044.

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Pour faire face à la croissance exponentielle du trafic des données mobiles, une solution possible est d'exploiter les larges bandes spectrales disponibles dans la partie millimétrique du spectre électromagnétique. Cependant, le signal transmis est fortement atténué, impliquant une portée de propagation limitée et un faible nombre des trajets de propagation (canal parcimonieux). Par conséquent, des faisceaux directifs doivent être utilisés pour focaliser l'énergie du signal transmis vers son utilisateur et compenser les pertes de propagation. Ces faisceaux ont besoin d'être dirigés convenablem

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34

Hao, Jialin. "Machine learning for road active safety in vehicular networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. http://www.theses.fr/2024IPPAS003.

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Cette thèse porte sur le développement d'une manœuvre d'aide au changement de voie (lane Change Assistance, LCA) sûre et efficace dans le contexte des réseaux de véhicules assistés par drones (Drone Assisted Vehicular Network, DAVN). En effet, les changements de voie contribuent de manière significative aux accidents de la route, nécessitant des solutions efficaces au sein des réseaux routiers. Les LCA stratégies actuelles établies sur l'apprentissage par renforcement profond (Deep Reinforcement Learning, DRL) sont limitées par les informations locales sur les véhicules, négligeant une vue glo

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35

Roth, Ana Lúcia. "Proposição de esquema conceitual para a governança de redes de cooperação federadas." Universidade do Vale do Rio dos Sinos, 2011. http://www.repositorio.jesuita.org.br/handle/UNISINOS/5187.

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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-04-18T18:05:24Z No. of bitstreams: 1 ANA LÚCIA ROTH_.pdf: 1643482 bytes, checksum: 4e4cda2780cc7255c2502065566263c6 (MD5)<br>Made available in DSpace on 2016-04-18T18:05:24Z (GMT). No. of bitstreams: 1 ANA LÚCIA ROTH_.pdf: 1643482 bytes, checksum: 4e4cda2780cc7255c2502065566263c6 (MD5) Previous issue date: 2011-08-30<br>Nenhuma<br>O presente trabalho versa sobre a governança de Redes de Cooperação Federadas, uma tipologia específica das Relações Interorganizacionais. O estudo buscou, motivado pelas lacunas teóricas

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Toofanee, Mohammud Shaad Ally. "An innovative ecosystem based on deep learning : Contributions for the prevention and prediction of diabetes complications." Electronic Thesis or Diss., Limoges, 2023. https://aurore.unilim.fr/theses/nxfile/default/656b0a1f-2ff2-49c5-bb3e-f34704d6f6b0/blobholder:0/2023LIMO0107.pdf.

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En 2021, le diabète touchait environ 537 millions de personnes dans le monde. Ce chiffre devrait grimper à 643 millions d'ici 2030 et 783 millions d'ici 2045. Le diabète est une maladie métabolique persistante qui nécessite des soins et une gestion quotidiens continus. Le fardeau des maladies chroniques pèse lourdement sur les systèmes de santé lorsqu'il touche une partie substantielle de la population. De telles circonstances ont un impact négatif non seulement sur le bien-être général d'une grande partie de la population, mais contribuent également de manière significative aux dépenses de sa

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Zari, Oualid. "Machine learning and privacy protection : Attacks and defenses." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS027.

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L'adoption croissante d'algorithmes d'apprentissage automatique dans des domaines sensibles à la protection de la vie privée a révolutionné l'analyse des données dans de nombreux domaines. Ces algorithmes, notamment les réseaux neuronaux profonds, l'analyse en composantes principales (PCA) et les réseaux neuronaux graphiques (GNN), traitent de grandes quantités de données pour en extraire des informations utiles et précieuses. L'intégration de ces techniques dans des applications critiques traitant des informations sensibles, comme des dossiers médicaux ou des données des réseaux sociaux, a pe

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Yu-Ting, Lai, and 賴瑀庭. "Design and Development of a Resource Control Framework for Federated Network Testbed." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/56556564524408044895.

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碩士<br>國立成功大學<br>電腦與通信工程研究所<br>103<br>With the rapid development of cloud computing and virtualization techniques, the needs for applications to use multiple resources distributed in different network testbeds, a.k.a resource sites have also grown greatly. With this demands, an improved and scalable Resource Control Framework is required to replace the traditional management orchestration used by each resource site. This adaptive Resource Control Framework is supposed to offer a mechanism to dynamically allocate and join physical resources located in varied sites according requirements from app

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Lin, Bor-Hsuin, and 林伯勳. "A study on Spatial Data Transformation Format and Network Communication Under Federated GIS." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/00809396105064601402.

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碩士<br>國立成功大學<br>測量工程學系<br>84<br>Among the governmental agencies, there exist many land-related information systems well operated at the present moment. These systems reveal common characteristics. That is they are developed solely according to the requirements of the agencies developing them. Each system is designed and tuned to fully meet the particular needs of the specific agency. When it comes to users from other agencies that have to access the systems, many problems arise. For i

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Esteves, Leonardo Galveias. "Federated Learning for IoT Edge Computing: An Experimental Study." Master's thesis, 2022. http://hdl.handle.net/10316/99424.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>Os dados gerados por anualmente rondam os 40 trilhões de gigabytes. Este aumento significativo de dados todos os anos trás a necessidade de assegurar a proteção de informação sensível. A Inteligência Artificial tem vindo a melhorar cada vez mais os seus resultados, apresentando modelos capazes de responder rigorosamente em áreas de atuação críticas, por exemplo, medicina, veículos autónomos, robótica, etc. Estes algoritmos precisam de enormes quantidades de dados

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Thomas, Paul. "Server characterisation and selection for personal metasearch." Phd thesis, 2008. http://hdl.handle.net/1885/150244.

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Eirinha, Tiago Filipe Rodrigues. "Extended Federated Social Networks in Environmental Sustainability." Master's thesis, 2017. http://hdl.handle.net/11110/1583.

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A enorme e rápida adesão ao uso das redes sociais, leva-nos a inferir que representam um dos mais relevantes fenómenos ligados à internet e às Tecnologias de Informação e Comunicação (TIC) em geral. Na generalidade dos casos, sempre que um comportamento assenta num rápido “crescimento”, um conjunto de questões serão levantadas. As redes sociais federadas surgiram no seguimento deste fenómeno, procurando colmatar algumas das lacunas identificadas nas redes sociais atuais, nomeadamente ao nível da portabilidade dos dados, da identidade, da referenciação e da privacidade. Após uma análise cuidada

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(10725357), Siddharth Divi. "UNIFYING DISTILLATION WITH PERSONALIZATION IN FEDERATED LEARNING." Thesis, 2021.

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<div>Federated learning (FL) is a decentralized privacy-preserving learning technique in which clients learn a joint collaborative model through a central aggregator without sharing their data. In this setting, all clients learn a single common predictor (FedAvg), which does not generalize well on each client's local data due to the statistical data heterogeneity among clients. In this paper, we address this problem with PersFL, a discrete two-stage personalized learning algorithm. In the first stage, PersFL finds the optimal teacher model of each client during the FL training phase. In the se

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Hark, Rhaban Simon. "Monitoring Federated Softwarized Networks: Approaches for Efficient and Collaborative Data Collection in Large-Scale Software-Defined Networks." Phd thesis, 2019. https://tuprints.ulb.tu-darmstadt.de/9073/1/2019-08-15_Hark_Rhaban_Simon.pdf.

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The term Softwarized Networking encapsulates technologies that allow the use of software to program a communication network. These technologies, predominantly Software-Defined Networking (SDN) and Network Functions Virtualization (NFV), have dominated the scientific interests of the networking community in the last decade. Leading companies already adopted SDN in large-scale deployments (e.g., Google’s B4 Project, Microsoft Azure). According to Cisco, 76% of all data centers will apply SDN by 2021. Along with a hand full of valuable advantages, the foundation of the success of Softwarized Ne

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LIN, CHIA-YEN, and 林佳延. "Application Study of Federated Unscented Kalman Filter on Dual-Band Infrared Search and Track Sensor Networks." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/926469.

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碩士<br>育達科技大學<br>資訊管理所<br>106<br>In recent years, unmanned aerial photographing of flying robots has become increasingly popular, and accidents have occurred due to improper operation of personnel, and even shooting in military forbidden areas has been heard. In view of this, the government revised the relevant laws to regulate the prohibition of over flight. When enforcing the law, the current policy must be reported by the public or patrolled by relevant law enforcement agencies personnel to the relevant locations. In order to save time, manpower, cost, and control of electronic evidence, mul

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Snopová, Karolína. "Činnost Evropské cyklistické federace jako součást evropské turistiky." Master's thesis, 2016. http://www.nusl.cz/ntk/nusl-342064.

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Title: Activities of the European Cyclists' Federation as a part of European tourism Objectives: The thesis is focused on cycling tourism in relation to European tourism, European Union policy and global policy of the world organizations. The aim is to present the activities of the European Cyclists' Federation, characterize the organization and analyze its biggest project - the development of the European network of long-distance cycling routes EuroVelo. This case study also shows the way of cooperation of a non-governmental organization, the European Union and world organizations in order to

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Vu, Thanh Tung. "Collaborative processing and radio resource management for cloud-based radio access networks." Thesis, 2021. http://hdl.handle.net/1959.13/1430295.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Next-generation wireless communication systems are expected to deliver extreme improvements in throughput and energy efficiency, in an era of pervasive machine learning applications. Meeting the ambitious technical objectives set out in beyond-5G visions requires a collaborative effort among a large number of wireless access points. Leveraging advances in optimisation theory, this thesis studies collaborative radio resource management for the two most promising cloud-based wireless network architectures: Cloud Radio Access Networks (C-RANs) an

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