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Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

Peng Yang, Ting Wang, Haibin Cai, Yuanming Shi, Chunxiao Jiang, Linling Kuang

TL;DR

The paper tackles energy-efficient, low-latency on-board learning in Space-CPN by combining brain-inspired SNNs with RelaySum-based inter-plane aggregation. It provides a theoretical convergence analysis showing sublinear rates tied to the inter-plane routing diameter, and introduces a routing-tree optimization to minimize this diameter via a minimum-diameter spanning-tree (MDST) formulation. Empirical results on EuroSAT demonstrate RelaySum outperforms gossip and all-reduce in convergence speed and accuracy, while SNNs deliver substantial energy savings. Collectively, the work offers a practical, privacy-preserving framework for scalable on-board learning in next-generation satellite networks.

Abstract

Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.

Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

TL;DR

The paper tackles energy-efficient, low-latency on-board learning in Space-CPN by combining brain-inspired SNNs with RelaySum-based inter-plane aggregation. It provides a theoretical convergence analysis showing sublinear rates tied to the inter-plane routing diameter, and introduces a routing-tree optimization to minimize this diameter via a minimum-diameter spanning-tree (MDST) formulation. Empirical results on EuroSAT demonstrate RelaySum outperforms gossip and all-reduce in convergence speed and accuracy, while SNNs deliver substantial energy savings. Collectively, the work offers a practical, privacy-preserving framework for scalable on-board learning in next-generation satellite networks.

Abstract

Satellite networks are able to collect massive space information with advanced remote sensing technologies, which is essential for real-time applications such as natural disaster monitoring. However, traditional centralized processing by the ground server incurs a severe timeliness issue caused by the transmission bottleneck of raw data. To this end, Space Computing Power Networks (Space-CPN) emerges as a promising architecture to coordinate the computing capability of satellites and enable on board data processing. Nevertheless, due to the natural limitations of solar panels, satellite power system is difficult to meet the energy requirements for ever-increasing intelligent computation tasks of artificial neural networks. To tackle this issue, we propose to employ spiking neural networks (SNNs), which is supported by the neuromorphic computing architecture, for on-board data processing. The extreme sparsity in its computation enables a high energy efficiency. Furthermore, to achieve effective training of these on-board models, we put forward a decentralized neuromorphic learning framework, where a communication-efficient inter-plane model aggregation method is developed with the inspiration from RelaySum. We provide a theoretical analysis to characterize the convergence behavior of the proposed algorithm, which reveals a network diameter related convergence speed. We then formulate a minimum diameter spanning tree problem on the inter-plane connectivity topology and solve it to further improve the learning performance. Extensive experiments are conducted to evaluate the superiority of the proposed method over benchmarks.

Paper Structure

This paper contains 22 sections, 6 theorems, 65 equations, 11 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. LEO Constellation
  4. Decentralized Satellite Learning
  5. Algorithm Design
  6. Brain-Inspired Spiking Neural Network
  7. RelaySum-based Inter-Plane Aggregation
  8. Theoretical Analysis
  9. Preliminaries
  10. Convergence Analysis
  11. System Optimization
  12. Problem Formulation
  13. Routing Tree Optimization
  14. Experimental Results
  15. Effectiveness of Proposed Inter-Plane Aggregation Scheme
  16. ...and 7 more sections

Key Result

Theorem 1

Minimizing the loss of the noisy neural network $f^{n}$ can be approximated into minimizing the loss of the embedded SNN $f^{s}$, regularized by the layerwise distance between the surrogate $H(\cdot)$ and the Heaviside function $\Theta(\cdot)$: where $L$ denotes the total layer of the neural network, $D^{l}$ is a constant related to the second derivative of the $l$-layer, $U^{l}$ represents the i

Figures (11)

  • Figure 1: A general decentralized satellite learning system.
  • Figure 2: Illustration of the leaky integrate-and-fire (LIF) neuron model.
  • Figure 3: Different inter-plane model aggregation schemes.
  • Figure 4: The $50/5/1$ Walker Star Constellation.
  • Figure 5: Comparison of different inter-plane model aggregation schemes.
  • ...and 6 more figures

Theorems & Definitions (8)

  • Theorem 1
  • Lemma 1
  • Lemma 2
  • Lemma 3
  • Lemma 4
  • Theorem 2
  • Definition 1
  • Definition 2