Communication-Efficient Learning for Satellite Constellations

2025-11-25

AI Summary

To address the low efficiency of federated learning (FL) in low-Earth-orbit (LEO) satellite constellations caused by bandwidth-constrained satellite-to-ground communications, this paper proposes a communication-efficient, algorithm-agnostic error-feedback FL framework. The method performs local iterative training and gradient compression onboard satellites, and introduces a provably convergent error-compensation mechanism prior to ground-station aggregation to dynamically correct compression-induced bias. Theoretical analysis guarantees convergence under non-i.i.d. data distributions. Simulations—based on realistic orbital dynamics and wireless link models—demonstrate a 42% reduction in communication overhead and a 3.8% improvement in model accuracy over baseline approaches, effectively balancing communication efficiency and model precision. The core innovation lies in decoupling the compression operator from the error-feedback design, enabling compatibility with arbitrary compression schemes and facilitating lightweight onboard computation alongside efficient ground-based aggregation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Agent Communication

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Designing communication-efficient learning algorithms for satellite constellations
• Reducing communication frequency and data size with ground stations
• Developing error feedback mechanisms to maintain model accuracy

Innovation

• Federated learning approach for satellite constellations
• Communication-efficient algorithm with local training
• Error feedback mechanism enhancing model accuracy