将联邦学习与量子联邦学习引入空天地一体化网络，提升智能应用的隐私与效率。
From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks
- 基于联邦学习与量子联邦学习实现跨空天地网络的分布式智能训练。
- 在无人机网络中验证量子联邦学习优于传统联邦学习的性能优势。
- 面向6G未来，适合关注隐私计算与智能网络融合的研究者阅读。
6G无线网络有望实现覆盖空、天、地及水下网络的无缝数据连接。作为未来6G的核心组成部分，空-天-地一体化网络（SAGIN）被设想用于支持海量实时智能应用。为实现这一目标，将AI技术融入SAGIN是必然趋势。由于SAGIN具有分布式与异构性特点，联邦学习（FL）及其延伸的量子联邦学习（QFL）正成为实现未来隐私保护与计算高效的SAGIN模型训练的重要技术。本文探讨了在SAGIN中应用FL/QFL的前景，列举了若干由FL与QFL融合驱动的代表性应用。以无人机网络为例，展示了量子增强训练方法相较于传统FL基准的优越性。同时，本文也指出了未来在QFL应用于SAGIN过程中面临的研究挑战与标准化问题。
原文摘要 · Abstract (English)
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.
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