On-Demand Client Deployment And Selection In Federated Learning

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dc.contributor.author Chahoud, Mario
dc.date.accessioned 2022-10-27T11:02:22Z
dc.date.available 2022-10-27T11:02:22Z
dc.date.copyright 2022 en_US
dc.date.issued 2022-08-18
dc.identifier.uri http://hdl.handle.net/10725/14146
dc.description.abstract Traditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients on the fly wherever and whenever needed. In fact, some devices are not available to serve as clients in the federated learning due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in federated learning offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed. en_US
dc.language.iso en en_US
dc.subject Machine learning -- Case studies en_US
dc.subject Computer networks -- Security measures en_US
dc.subject Internet of things -- Security measures en_US
dc.subject Mobile communication systems en_US
dc.subject Data privacy en_US
dc.subject Lebanese American University -- Dissertations en_US
dc.subject Dissertations, Academic en_US
dc.title On-Demand Client Deployment And Selection In Federated Learning en_US
dc.type Thesis en_US
dc.term.submitted Summer en_US
dc.author.degree MS in Computer Science en_US
dc.author.school SAS en_US
dc.author.idnumber 201604598 en_US
dc.author.commembers Otoum, Safa
dc.author.commembers Haraty, Ramzi
dc.author.department Computer Science And Mathematics en_US
dc.description.physdesc 1 online resource (x, 49 leaves): col. ill. en_US
dc.author.advisor Mourad, Azzam
dc.keywords IOT en_US
dc.keywords Federated Learning en_US
dc.keywords Privacy en_US
dc.keywords Client Selection en_US
dc.keywords On-Demand Client deployment en_US
dc.keywords Containers en_US
dc.keywords Docker en_US
dc.keywords Kubernetes en_US
dc.keywords Kubeadm en_US
dc.description.bibliographiccitations Bibliography: leaves 46-49. en_US
dc.identifier.doi https://doi.org/10.26756/th.2022.474
dc.author.email mario.chahoud@lau.edu.lb en_US
dc.identifier.tou http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php en_US
dc.publisher.institution Lebanese American University en_US
dc.author.affiliation Lebanese American University en_US

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