Communication-Efficient Learning for Satellite Constellations

TL;DR

This paper introduces a novel, communication-efficient federated learning algorithm tailored for satellite constellations, combining local training, compression, and error feedback to improve accuracy and reduce communication overhead in space scenarios.

Contribution

The paper presents a new federated learning algorithm for satellite networks that reduces communication costs while maintaining high accuracy, including an error feedback mechanism and convergence analysis.

Findings

Superior performance in realistic simulations

Effective reduction in communication overhead

Enhanced accuracy through error feedback

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.