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Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

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Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process

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dc.contributor.author Dahmane, Sofiane es_ES
dc.contributor.author Yagoubi, Mohamed Bachir es_ES
dc.contributor.author Brik, Bouziane es_ES
dc.contributor.author Kerrache, Chaker Abdelaziz es_ES
dc.contributor.author Tavares De Araujo Cesariny Calafate, Carlos Miguel es_ES
dc.contributor.author Lorenz, Pascal es_ES
dc.date.accessioned 2023-05-08T18:02:08Z
dc.date.available 2023-05-08T18:02:08Z
dc.date.issued 2022-07 es_ES
dc.identifier.uri http://hdl.handle.net/10251/193215
dc.description.abstract [EN] Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs' privacy. However, UAV devices are usually limited in terms of resources such as battery, memory, and CPU. Some of the UAV devices may not be able to build a local learning models due to their resources capacity. Hence, there is a great need to select the adequate UAVs at each FL round, that are able to build a local DL model based on their resource capacities. In this paper, we design a novel and S-MEC-enabled framework that optimizes the selection of UAV participants at each FL training round, named FedSel. FedSel considers the available UAVs along with their resource capacities, in terms of energy, CPU, and memory, to determine which UAV device is able to participant in the FL process. Thus, we formulate the UAV selection problem as an Integer Linear Program, which considers the aforementioned constraints. We also prove that this problem is NP-hard, and suggest a Tabu Search (TS) metaheuristic-based approach to resolve it. Moreover, FedSel is built on top of blockchain technology, in order to ensure a secure selection of UAV participants, and hence building reliable FL-based models. Simulation results validate the efficiency of our FedSel scheme in balancing computational load among available UAVs and optimizing the UAV selection process. es_ES
dc.description.sponsorship This work is derived from R&D project RTI2018-096384-B-I00, funded by MCIN/AEI/10.13039/501100011033 and "ERDF A way of making Europe". es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject UAV networks es_ES
dc.subject Federated Deep Learning es_ES
dc.subject UAVs selection es_ES
dc.subject Blockchain es_ES
dc.subject Edge computing es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics11142119 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//RTI2018-096384-B-I00-AR//SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Dahmane, S.; Yagoubi, MB.; Brik, B.; Kerrache, CA.; Tavares De Araujo Cesariny Calafate, CM.; Lorenz, P. (2022). Multi-Constrained and Edge-Enabled Selection of UAV Participants in Federated Learning Process. Electronics. 11(14):1-14. https://doi.org/10.3390/electronics11142119 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics11142119 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 14 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11 es_ES
dc.description.issue 14 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\468678 es_ES
dc.contributor.funder AGENCIA ESTATAL DE INVESTIGACION es_ES
dc.contributor.funder European Regional Development Fund es_ES


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