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Federated learning meets remote sensing

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dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorSanchez Fernandez, Andres J.
dc.contributor.authorRico Gallego, Juan Antonio
dc.contributor.authorhan, lirong
dc.contributor.authorHaut, Juan M.
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0001-6743-3570
dc.contributor.orcidhttps://orcid.org/0000-0002-4264-7473
dc.contributor.orcidhttps://orcid.org/0000-0002-8613-7037
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.date.accessioned2024-11-20T09:43:52Z
dc.date.available2024-11-20T09:43:52Z
dc.date.issued2024-12-01
dc.descriptionThe registered version of this article, first published in “Expert Systems with Applications, Volume 255, 2024", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.eswa.2024.124583 La versión registrada de este artículo, publicado por primera vez en “Expert Systems with Applications, Volume 255, 2024", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.eswa.2024.124583
dc.description.abstractRemote sensing (RS) imagery provides invaluable insights into characterizing the Earth’s land surface within the scope of Earth observation (EO). Technological advances in capture instrumentation, coupled with the rise in the number of EO missions aimed at data acquisition, have significantly increased the volume of accessible RS data. This abundance of information has alleviated the challenge of insufficient training samples, a common issue in the application of machine learning (ML) techniques. In this context, crowd-sourced data play a crucial role in gathering diverse information from multiple sources, resulting in heterogeneous datasets that enable applications to harness a more comprehensive spatial coverage of the surface. However, the sensitive nature of RS data requires ensuring the privacy of the complete collection. Consequently, federated learning (FL) emerges as a privacy-preserving solution, allowing collaborators to combine such information from decentralized private data collections to build efficient global models. This paper explores the convergence between the FL and RS domains, specifically in developing data classifiers. To this aim, an extensive set of experiments is conducted to analyze the properties and performance of novel FL methodologies. The main emphasis is on evaluating the influence of such heterogeneous and disjoint data among collaborating clients. Moreover, scalability is evaluated for a growing number of clients, and resilience is assessed against Byzantine attacks. Finally, the work concludes with future directions and serves as the opening of a new research avenue for developing efficient RS applications under the FL paradigm. The source code is publicly available at https://github.com/hpc-unex/FLmeetsRS.en
dc.description.versionversión publicada
dc.identifier.citationSergio Moreno-Álvarez, Mercedes E. Paoletti, Andres J. Sanchez-Fernandez, Juan A. Rico-Gallego, Lirong Han, Juan M. Haut, Federated learning meets remote sensing, Expert Systems with Applications, Volume 255, Part B, 2024, 124583, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2024.124583.
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2024.124583
dc.identifier.issn0957-4174
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24443
dc.journal.issuepart B
dc.journal.titleExpert Systems with Applications
dc.journal.volume225
dc.language.isoen
dc.page.initial124583
dc.publisherELSEVIER
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsFederated learningen
dc.subject.keywordsRemote sensingen
dc.subject.keywordsCrowd-sourced dataen
dc.subject.keywordsEarth observationen
dc.subject.keywordsDeep neural networksen
dc.subject.keywordsImage classificationen
dc.titleFederated learning meets remote sensingen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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