Intelligent Bilateral Client Selection in Federated Learning Using Game Theory
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborat...
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| Format: | masterThesis |
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2022
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| Online Access: | http://hdl.handle.net/10725/14123 https://doi.org/10.26756/th.2022.451 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
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| _version_ | 1864513468818784256 |
|---|---|
| author | Wehbi, Osama |
| author_facet | Wehbi, Osama |
| author_role | author |
| dc.creator.none.fl_str_mv | Wehbi, Osama |
| dc.date.none.fl_str_mv | 2022-10-25T07:02:52Z 2022-10-25T07:02:52Z 2022 2022-08-18 |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10725/14123 https://doi.org/10.26756/th.2022.451 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |
| dc.language.none.fl_str_mv | en |
| dc.publisher.none.fl_str_mv | Lebanese American University |
| dc.rights.*.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Machine learning -- Case studies Game theory Internet of things Computational intelligence Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| dc.title.none.fl_str_mv | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| dc.type.none.fl_str_mv | Thesis info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/masterThesis |
| description | Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaF selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model. |
| eu_rights_str_mv | openAccess |
| format | masterThesis |
| id | LAURepo_c4ab11c9a9dbfcc96effc60e5d76973a |
| language_invalid_str_mv | en |
| network_acronym_str | LAURepo |
| network_name_str | Lebanese American University repository |
| oai_identifier_str | oai:laur.lau.edu.lb:10725/14123 |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Lebanese American University |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
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| spelling | Intelligent Bilateral Client Selection in Federated Learning Using Game TheoryWehbi, OsamaMachine learning -- Case studiesGame theoryInternet of thingsComputational intelligenceData privacyLebanese American University -- DissertationsDissertations, AcademicFederated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves designing (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the new connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaF selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.1 online resource (x, 45 leaves): col. ill.Bibliography: leaves 42-45.Lebanese American University2022-10-25T07:02:52Z2022-10-25T07:02:52Z20222022-08-18Thesisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesishttp://hdl.handle.net/10725/14123https://doi.org/10.26756/th.2022.451http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.phpeninfo:eu-repo/semantics/openAccessoai:laur.lau.edu.lb:10725/141232022-10-25T07:03:59Z |
| spellingShingle | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory Wehbi, Osama Machine learning -- Case studies Game theory Internet of things Computational intelligence Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| status_str | publishedVersion |
| title | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| title_full | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| title_fullStr | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| title_full_unstemmed | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| title_short | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| title_sort | Intelligent Bilateral Client Selection in Federated Learning Using Game Theory |
| topic | Machine learning -- Case studies Game theory Internet of things Computational intelligence Data privacy Lebanese American University -- Dissertations Dissertations, Academic |
| url | http://hdl.handle.net/10725/14123 https://doi.org/10.26756/th.2022.451 http://libraries.lau.edu.lb/research/laur/terms-of-use/thesis.php |