Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks

doi:arXiv:2501.15995

	Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks
	Yang, Peng 1; Wang, Ting 1; Cai, Haibin 1; Shi, Yuanming2 ; Jiang, Chunxiao 3; Kuang, Linling 3
	2025-01-27
状态	已发表
摘要	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.
语种	英语
DOI	arXiv:2501.15995
相关网址	查看原文
出处	Arxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:120965571
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware& Architecture ; Engineering, Electrical& Electronic
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/507033
专题	信息科学与技术学院信息科学与技术学院_PI研究组_石远明组
通讯作者	Yang, Peng
作者单位	1.East China Normal Univ, MoE Engn Res Ctr Software Hardware Codesign Technol & Applicat, Shanghai Key Lab Trustworthy Comp, Shanghai 200062, Peoples R China 2.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 3.Tsinghua Univ, Beijing Natl Res Ctr Informat Sci & Technol, Beijing 100084, Peoples R China
推荐引用方式 GB/T 7714	Yang, Peng,Wang, Ting,Cai, Haibin,et al. Brain-Inspired Decentralized Satellite Learning in Space Computing Power Networks. 2025.