用量子纠缠实现卫星间无中心协同训练，提升低轨星座学习效率。
orb-QFL: Orbital Quantum Federated Learning
- 利用量子纠缠与本地量子计算实现卫星间去中心化协作。
- 在星载环境下连续更新模型，应对间歇连接与高延迟挑战。
- 适合低轨卫星群、资源受限的分布式学习场景。
近期量子计算的突破为联邦学习（FL）带来变革性机遇，尤其在通信与协调受限的非地面环境中。本文提出面向低地球轨道（LEO）卫星星座的量子辅助联邦学习框架orb-QFL。该框架摒弃传统集中式服务器与全局聚合机制（如FedAvg），转而利用量子纠缠和本地量子处理，实现卫星间的去中心化协作。此设计天然应对轨道动态带来的间歇连接、高传播延迟与覆盖变化等问题。通过相邻卫星间的量子同步，实现持续模型优化，增强系统韧性并保护数据本地性。为验证方法，我们结合Qiskit量子机器学习工具包与Poliastro轨道模拟，基于Statlog数据集开展实验。
原文摘要 · Abstract (English)
Recent breakthroughs in quantum computing present transformative opportunities for advancing Federated Learning (FL), particularly in non-terrestrial environments characterized by stringent communication and coordination constraints. In this study, we propose orbital QFL, termed orb-QFL, a novel quantum-assisted Federated Learning framework tailored for Low Earth Orbit (LEO) satellite constellations. Distinct from conventional FL paradigms, termed orb-QFL operates without centralized servers or global aggregation mechanisms (e.g., FedAvg), instead leveraging quantum entanglement and local quantum processing to facilitate decentralized, inter-satellite collaboration. This design inherently addresses the challenges of orbital dynamics, such as intermittent connectivity, high propagation delays, and coverage variability. The framework enables continuous model refinement through direct quantum-based synchronization between neighboring satellites, thereby enhancing resilience and preserving data locality. To validate our approach, we integrate the Qiskit quantum machine learning toolkit with Poliastro-based orbital simulations and conduct experiments using Statlog dataset.
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