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Correlation-Aware Averaging for Federated Learning in Remote Sensing Data Classification

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dc.contributor.authorMoreno Álvarez, Sergio
dc.contributor.authorhan, lirong
dc.contributor.authorPaoletti, Mercedes Eugenia
dc.contributor.authorHaut, Juan Mario
dc.contributor.orcidhttps://orcid.org/0000-0002-8613-7037
dc.contributor.orcidhttps://orcid.org/0000-0003-1030-3729
dc.contributor.orcidhttps://orcid.org/0000-0001-6701-961X
dc.coverage.spatialAthens, Greece
dc.coverage.temporal2024-07-12
dc.date.accessioned2024-11-21T08:05:09Z
dc.date.available2024-11-21T08:05:09Z
dc.date.issued2024
dc.descriptionThe registered version of this article, first published in “IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium", is available online at the publisher's website: IEEE, https://doi.org/10.1109/IGARSS53475.2024.10641628 La versión registrada de este artículo, publicado por primera vez en “IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium", está disponible en línea en el sitio web del editor: IEEE, https://doi.org/10.1109/IGARSS53475.2024.10641628
dc.description.abstractThe increasing volume of remote sensing (RS) data offers substantial benefits for the extraction and interpretation of features from these scenes. Indeed, the detection of distinguishing features among captured materials and objects is crucial for classification purposes, such as in environmental monitoring applications. In these algorithms, the classes characterized by lower correlation often exhibit more distinct and discernible features, facilitating their differentiation in a straightforward manner. Nevertheless, the rise of Big Data provides a wide range of data acquired through multiple decentralized devices, where its susceptibility to be shared among various users or clients presents challenges in safeguarding privacy. Meanwhile, global features for similar classes are required to be learned for generalization purposes in the classification process. To address this, federated learning (FL) emerges as a privacy efficient decentralized solution. Firstly, in such scenarios, proprietary data is held by individual clients participating in the training of a global model. Secondly, clients may encounter challenges in identifying features that are more distinguishable within the data distributions of other clients. In this study, in order to handle these challenges, a novel methodology is proposed that considers the least correlated classes (LCCs) included in each client data distribution. This strategy exploits the distinctive features between classes, thereby enhancing performance and generalization ability in a secure and private environment.en
dc.description.versionversión publicada
dc.identifier.citationS. Moreno-Álvarez, L. Han, M. E. Paoletti and J. M. Haut, "Correlation-Aware Averaging for Federated Learning in Remote Sensing Data Classification," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 8708-8711, doi: 10.1109/IGARSS53475.2024.10641628
dc.identifier.doihttps://doi.org/10.1109/IGARSS53475.2024.10641628
dc.identifier.isbn979-8-3503-6032-5
dc.identifier.issn2153-6996, eISSN 2153-7003
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24459
dc.language.isoen
dc.publisherIEEE
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.congressIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
dc.relation.departmentLenguajes y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 Informática
dc.subject.keywordsTrainingen
dc.subject.keywordsAdaptation modelsen
dc.subject.keywordsPrivacyen
dc.subject.keywordsAccuracyen
dc.subject.keywordsFederated learningen
dc.subject.keywordsNeural networksen
dc.subject.keywordsFeature extractionen
dc.titleCorrelation-Aware Averaging for Federated Learning in Remote Sensing Data Classificationen
dc.typeactas de congresoes
dc.typeconference proceedingsen
dspace.entity.typePublication
relation.isAuthorOfPublication3482d7bc-e120-48a3-812e-cc4b25a6d2fe
relation.isAuthorOfPublication.latestForDiscovery3482d7bc-e120-48a3-812e-cc4b25a6d2fe
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