A Snapshot of the Frontiers of Client Selection in Federated Learning

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dc.contributorLaboratorio de Investigación en Visión Móvil (MVRLab)es_ES
dc.contributor.authorNémeth, Gergely Dániel-
dc.contributor.authorLozano, Miguel Angel-
dc.contributor.authorQuadrianto, Novi-
dc.contributor.authorOliver, Nuria-
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales_ES
dc.date.accessioned2024-03-08T12:41:11Z-
dc.date.available2024-03-08T12:41:11Z-
dc.date.issued2022-12-
dc.identifier.citationTransactions on Machine Learning Research. 2022, 12es_ES
dc.identifier.issn2835-8856-
dc.identifier.urihttp://hdl.handle.net/10045/141323-
dc.description.abstractFederated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model’s parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as an approach to limit the number of communicating parties at every step of the process. Since the early naïve random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.es_ES
dc.description.sponsorshipG.D.N. and N.O. have been partially supported by funding received at the ELLIS Unit Alicante Foundation from the Regional Government of Valencia in Spain (Generalitat Valenciana, Conselleria d’Innovació, Universitats, Ciència i Societat Digital, Dirección General para el Avance de la Sociedad Digital). G.D.N. is also funded by a grant by the Banco Sabadell Foundation. N.Q. has been supported in part by a European Research Council (ERC) Starting Grant for the project “Bayesian Models and Algorithms for Fairness and Transparency”, funded under the European Union’s Horizon 2020 Framework Programme (grant agreement no. 851538).es_ES
dc.languageenges_ES
dc.publisherTMLRes_ES
dc.rights© TMLRes_ES
dc.subjectFederated learninges_ES
dc.subjectMachine learninges_ES
dc.subjectClient selectiones_ES
dc.subjectTaxonomyes_ES
dc.titleA Snapshot of the Frontiers of Client Selection in Federated Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.relation.publisherversionhttps://jmlr.org/tmlr/papers/es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/851538es_ES
Aparece en las colecciones:INV - MVRLab - Artículos de Revistas
Investigaciones financiadas por la UE

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