6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted.

翻译：6G无线网络预计将提供覆盖空天地及水下网络的无缝数据连接。作为未来6G网络的核心组成部分，空天地一体化网络（SAGIN）被设想用于支撑海量实时智能应用。为实现这一目标，将人工智能技术引入SAGIN已成为必然趋势。鉴于SAGIN分布式异构的网络架构，联邦学习（FL）及其量子化演进形式——量子联邦学习（QFL）正成为构建未来具备隐私增强与计算高效特性的SAGIN的关键AI模型训练技术。本文系统探讨了FL/QFL在SAGIN中的应用前景，展示了若干基于FL与QFL融合的典型应用场景，并通过无人机网络中的QFL案例研究，揭示了量子赋能训练方法相较于传统FL基准方案的优势。最后，本文着重分析了未来SAGIN中部署QFL所面临的研究挑战及标准化路径。