用区块链解决多厂商低轨卫星联邦学习的可信问题
Decentralized Trust for Space AI: Blockchain-Based Federated Learning Across Multi-Vendor LEO Satellite Networks
- 将共识机制迁移至高空平台，缓解卫星算力不足问题
- 实现跨轨道模型更新的可追溯性，防篡改与不完整提交
- 实测缩短30小时收敛时间，适合高实时性太空智能应用
空间人工智能正通过灾害检测、边境监控和气候监测等应用重塑政府与产业，依赖商业与政府低地球轨道（LEO）卫星产生的海量数据。联邦卫星学习（FSL）可在不共享原始数据的前提下联合训练模型，但受间歇性连接影响收敛缓慢，并存在严重信任风险——来自不同卫星星座的模型更新可能被恶意注入或伪造，尤其通过星间或星地通信链路的网络攻击。本文提出OrbitChain，一种基于区块链的框架，支持多供应商在LEO网络中可信协作。OrbitChain（i）将共识任务卸载至计算能力更强的高空平台（HAPs），（ii）确保来自不同供应商拥有的不同轨道模型更新具有透明可审计的来源记录，（iii）防止被操纵或不完整的贡献影响全局模型聚合。大量仿真表明，OrbitChain在降低计算与通信开销的同时，提升了隐私、安全性和全局模型准确率。其许可式权威证明（PoA）账本在1秒内完成超1000个区块确认（1/5、3/5、5/5法定人数下延迟分别为0.16秒、0.26秒、0.35秒）。此外，相较于单供应商方案，该框架在真实卫星数据集上将收敛时间最多缩短30小时，验证了其实时多供应商学习的有效性。代码已公开于https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git。
原文摘要 · Abstract (English)
The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO) satellites. Federated satellite learning (FSL) enables joint model training without sharing raw data, but suffers from slow convergence due to intermittent connectivity and introduces critical trust challenges--where biased or falsified updates can arise across satellite constellations, including those injected through cyberattacks on inter-satellite or satellite-ground communication links. We propose OrbitChain, a blockchain-backed framework that empowers trustworthy multi-vendor collaboration in LEO networks. OrbitChain (i) offloads consensus to high-altitude platforms (HAPs) with greater computational capacity, (ii) ensures transparent, auditable provenance of model updates from different orbits owned by different vendors, and (iii) prevents manipulated or incomplete contributions from affecting global FSL model aggregation. Extensive simulations show that OrbitChain reduces computational and communication overhead while improving privacy, security, and global model accuracy. Its permissioned proof-of-authority ledger finalizes over 1000 blocks with sub-second latency (0.16,s, 0.26,s, 0.35,s for 1-of-5, 3-of-5, and 5-of-5 quorums). Moreover, OrbitChain reduces convergence time by up to 30 hours on real satellite datasets compared to single-vendor, demonstrating its effectiveness for real-time, multi-vendor learning. Our code is available at https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git
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