Communication-Efficient Learning for Satellite Constellations

• URL: http://arxiv.org/abs/2511.20220v1
• Date: Tue, 25 Nov 2025 11:47:47 GMT
• Title: Communication-Efficient Learning for Satellite Constellations
• Authors: Ruxandra-Stefania Tudose, Moritz H. W. Grüss, Grace Ra Kim, Karl H. Johansson, Nicola Bastianello,
• Abstract summary: We focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models.<n>We employ several mechanisms to reduce the number of communications with the ground station.<n>We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Satellite constellations in low-Earth orbit are now widespread, enabling positioning, Earth imaging, and communications. In this paper we address the solution of learning problems using these satellite constellations. In particular, we focus on a federated approach, where satellites collect and locally process data, with the ground station aggregating local models. We focus on designing a novel, communication-efficient algorithm that still yields accurate trained models. To this end, we employ several mechanisms to reduce the number of communications with the ground station (local training) and their size (compression). We then propose an error feedback mechanism that enhances accuracy, which yields, as a byproduct, an algorithm-agnostic error feedback scheme that can be more broadly applied. We analyze the convergence of the resulting algorithm, and compare it with the state of the art through simulations in a realistic space scenario, showcasing superior performance.

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