ItemArticle

FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures

Title
FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures
Creator
Siddalingappa, Rashmi; Deepa, S.; Priya Stella Mary, I.; Kalpana, P.; B A, Lakshmi
Description
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.
Source
International Journal of Information Technology (Singapore);
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Federated Learning, Edge Computing, Machine Learning, Internet-of-Things, Deep Neural Network Architecture, Stochastic Gradient Descent
Coverage
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
Rights
All Open Access; Green Open Access; Hybrid Gold Open Access
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Siddalingappa, Rashmi; Deepa, S.; Priya Stella Mary, I.; Kalpana, P.; B A, Lakshmi, “FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22107.

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