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Communication-Efficient Learning for Satellite Constellations

Ruxandra-Stefania Tudose, Moritz H. W. Grüss, Grace Ra Kim, Karl H. Johansson, Nicola Bastianello

TL;DR

This work addresses learning from data generated by satellite constellations under bandwidth-limited conditions by formulating a federated learning objective over $N$ satellites with a ground station. It introduces a communication-efficient Fed-LT framework that combines local training, partial participation, bi-directional compression, and an algorithm-agnostic error-feedback mechanism, plus a satellite-ready extension Fed-LTSat that uses inter-satellite links to reduce ground transmissions. A convergence analysis is provided for $ ext{delta}$-approximate compressors under smooth, strongly convex losses, and extensive simulations in space-like scenarios demonstrate superior accuracy under communication constraints compared to state-of-the-art methods. The practical impact is enabling accurate onboard learning in space with reduced Earth communication, by integrating EF, compression, and ISL-enabled coordination while preserving convergence guarantees.

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.

Communication-Efficient Learning for Satellite Constellations

TL;DR

This work addresses learning from data generated by satellite constellations under bandwidth-limited conditions by formulating a federated learning objective over satellites with a ground station. It introduces a communication-efficient Fed-LT framework that combines local training, partial participation, bi-directional compression, and an algorithm-agnostic error-feedback mechanism, plus a satellite-ready extension Fed-LTSat that uses inter-satellite links to reduce ground transmissions. A convergence analysis is provided for -approximate compressors under smooth, strongly convex losses, and extensive simulations in space-like scenarios demonstrate superior accuracy under communication constraints compared to state-of-the-art methods. The practical impact is enabling accurate onboard learning in space with reduced Earth communication, by integrating EF, compression, and ISL-enabled coordination while preserving convergence guarantees.

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.

Paper Structure

This paper contains 11 sections, 1 theorem, 11 equations, 4 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Algorithm Design and Convergence Analysis
  3. Problem formulation
  4. Algorithm design: communication efficiency
  5. Algorithm design: satellite-ready version
  6. Convergence analysis
  7. Numerical Results
  8. Evaluating the impact of error feedback
  9. Comparison in a realistic space scenario
  10. Conclusions and Future Work
  11. Proof of Proposition 1

Key Result

Proposition 1

Let Assumptions as:loss and as:compressor hold. Assume that each agent $i$ is active at iteration $k$ with probability $p_i \in (0, 1]$. Assume that $\left\lVert x_{i,k} \right\rVert \leq \beta$. Then where $\bar{x} = \mathop{\mathrm{\arg\!\min}}\limits_{x \in \mathbb{R}^n} \sum_{i = 1}^N f_i(x)$, $\sigma \in (0, 1)$ is the convergence rate characterized in bastianello_enhancing_2024.

Figures (4)

  • Figure 1: Federated learning for a satellite constellation, with ground station acting as coordinator.
  • Figure 2: Scheme of the federated learning set-up.
  • Figure 3: Flow chart of the agnostic error-feedback mechanism.
  • Figure 4: Optimality error evolution over iterations, employing quantization with $V_{\min} = - 1$, $V_{\max} = 1$ and $L=10$

Theorems & Definitions (5)

  • Remark 1
  • Definition 1: $\delta$-approximate compressor
  • Proposition 1
  • Definition 2: Uniform quantization
  • Definition 3: Rand-$d$ sparsification