Non-terrestrial networks (NTNs) are emerging as a core component of future 6G communication systems, providing global connectivity and supporting data-intensive applications. In this paper, we propose a distributed hierarchical federated learning (HFL) framework within the NTN architecture, leveraging a high altitude platform station (HAPS) constellation as intermediate distributed FL servers. Our framework integrates both low-Earth orbit (LEO) satellites and ground clients in the FL training process while utilizing geostationary orbit (GEO) and medium-Earth orbit (MEO) satellites as relays to exchange FL global models across other HAPS constellations worldwide, enabling seamless, global-scale learning. The proposed framework offers several key benefits: (i) enhanced privacy through the decentralization of the FL mechanism by leveraging the HAPS constellation, (ii) improved model accuracy and reduced training loss while balancing latency, (iii) increased scalability of FL systems through ubiquitous connectivity by utilizing MEO and GEO satellites, and (iv) the ability to use FL data, such as resource utilization metrics, to further optimize the NTN architecture from a network management perspective. A numerical study demonstrates the proposed framework's effectiveness, with improved model accuracy, reduced training loss, and efficient latency management. The article also includes a brief review of FL in NTNs and highlights key challenges and future research directions.

本研究解决了未来6G通信系统中非地面网络（NTN）下的联邦学习（FL）应用中的效率和隐私问题。提出了一种分布式分层联邦学习框架，利用高空平台站（HAPS）作为中介服务器，整合了低轨道卫星和地面客户端的训练过程，显著提高了模型准确性，减少了训练损失，并有效管理了延迟。该框架的成功应用为未来的全球规模学习提供了可能性，且对提升网络架构的优化也具有重要意义。

非地面网络中的联邦学习：设计、架构与挑战