A Snapshot of the Frontiers of Client Selection in Federated Learning
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Título: | A Snapshot of the Frontiers of Client Selection in Federated Learning |
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Autor/es: | Németh, Gergely Dániel | Lozano, Miguel Angel | Quadrianto, Novi | Oliver, Nuria |
Grupo/s de investigación o GITE: | Laboratorio de Investigación en Visión Móvil (MVRLab) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | Federated learning | Machine learning | Client selection | Taxonomy |
Fecha de publicación: | dic-2022 |
Editor: | TMLR |
Cita bibliográfica: | Transactions on Machine Learning Research. 2022, 12 |
Resumen: | Federated 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. |
Patrocinador/es: | G.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). |
URI: | http://hdl.handle.net/10045/141323 |
ISSN: | 2835-8856 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © TMLR |
Revisión científica: | si |
Versión del editor: | https://jmlr.org/tmlr/papers/ |
Aparece en las colecciones: | INV - MVRLab - Artículos de Revistas Investigaciones financiadas por la UE |
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Nemeth_etal_2022_TMLR.pdf | 609,6 kB | Adobe PDF | Abrir Vista previa | |
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