# Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

arXiv: https://arxiv.org/abs/2501.15995 · HTML: https://mlsanity.com/paper/2501.15995/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 27 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [RelaySum for Decentralized Deep Learning on Heterogeneous Data](https://mlsanity.com/paper/2110.04175/) (2021)
- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [Ground-Assisted Federated Learning in LEO Satellite Constellations](https://mlsanity.com/paper/2109.01348/) (2021)
- [FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stations](https://mlsanity.com/paper/2202.01267/) (2022)
- [On-Board Federated Learning for Dense LEO Constellations](https://mlsanity.com/paper/2111.12769/) (2021)
- [Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2206.00307/) (2022)
- [AsyncFLEO: Asynchronous Federated Learning for LEO Satellite Constellations with High-Altitude Platforms](https://mlsanity.com/paper/2212.11522/) (2022)
- [Inter-Plane Inter-Satellite Connectivity in Dense LEO Constellations](https://mlsanity.com/paper/2005.07965/) (2020)
- [LEO Small-Satellite Constellations for 5G and Beyond-5G Communications](https://mlsanity.com/paper/1912.08110/) (2019)
- [Optimizing Federated Learning in LEO Satellite Constellations via Intra-Plane Model Propagation and Sink Satellite Scheduling](https://mlsanity.com/paper/2302.13447/) (2023)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications](https://mlsanity.com/paper/2401.06911/) (2024)
- [DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellations](https://mlsanity.com/paper/2407.05850/) (2024)
- [Energy-Aware Federated Learning in Satellite Constellations](https://mlsanity.com/paper/2409.14832/) (2024)

## Built on by (most cited first, 1 of 1)

- [ORBITALIF: An Efficient Spiking Federated Learning Framework for Onboard Cloud Removal](https://mlsanity.com/paper/2608.24073/) (2026)

## Abstract (arXiv)

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.