Moreno Álvarez, SergioPaoletti, Mercedes EugeniaSanchez Fernandez, Andres J.Rico Gallego, Juan Antoniohan, lirongHaut, Juan M.2024-11-202024-11-202024-12-01Sergio 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.2024.124583.0957-4174https://doi.org/10.1016/j.eswa.2024.124583https://hdl.handle.net/20.500.14468/24443The 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.2024.124583Remote 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.eninfo:eu-repo/semantics/openAccess12 Matemáticas::1203 Ciencia de los ordenadores ::1203.17 InformáticaartículoFederated learningRemote sensingCrowd-sourced dataEarth observationDeep neural networksImage classification