Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Siddalingappa, Rashmi; Deepa, S.; Priya Stella Mary, I.; Kalpana, P.; B A, Lakshmi Title A name given to the resource FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Information Technology (Singapore); Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s41870-025-03010-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s41870-025-03010-0</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105025426540?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105025426540?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siddalingappa R., Department of Computer and Data Science, York St John University, London, E14 2BA, United Kingdom; Deepa S., Department of Computer Science, Christ University, Karnataka, Bangalore, 560073, India; Priya Stella Mary I., Department of Computer Science, Christ University, Karnataka, Bangalore, 560073, India; Kalpana P., Department of Computer Science, Christ University, Karnataka, Bangalore, 560073, India; B A L., UST Global, Bangalore, India Description An account of the resource We introduce FEDGE: FEDerated Learning at the EDGE, a framework designed for efficient AI deployment in resource-constrained satellite constellations. FEDGE integrates federated learning with edge computing to address communication overhead and latency challenges in distributed space environments. The framework features a novel edge-enhanced ground station protocol that dynamically schedules model aggregation based on satellite-provided metadata, combined with local stochastic gradient descent training at satellite edge devices and gradient compression via quantization. Experimental validation on MNIST and EuroSAT datasets demonstrates the practical viability of the approach. On MNIST, FEDGE achieved 94.33% training accuracy with 0.21 loss and 90.05% test accuracy with 0.24 loss. On EuroSAT, the framework reached 93.47% training accuracy with 0.18 loss and 91.51% test accuracy with 0.21 loss. Gradient quantization reduces data exchange by up to 14 with approximately 4% impact on test loss. These results validate FEDGE as a communication-efficient solution for decentralized AI deployment in satellite systems, enabling autonomous spacecraft intelligence and addressing the unique constraints of space-based computing platforms. The Author(s) 2025. Subject The topic of the resource Federated Learning, Edge Computing, Machine Learning, Internet-of-Things, Deep Neural Network Architecture, Stochastic Gradient Descent Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 25112104; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Green Open Access; Hybrid Gold Open Access Format The file format, physical medium, or dimensions of the resource online