Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies intelligence over the whole network from the core to the edge, including end devices. Nevertheless, fulfilling this vision, particularly the intelligence at the edge, is extremely challenging due to the limited resources of edge devices as well as the ubiquitous coverage envisioned by 6G. To empower edge intelligence, in this article, we propose a framework called air-ground integrated federated learning (AGIFL), which organically integrates air-ground integrated networks and federated learning (FL). In AGIFL, leveraging the flexible on-demand 3D deployment of aerial nodes such as unmanned aerial vehicles (UAVs), all the nodes can collaboratively train an effective learning model by FL. We also conduct a case study to evaluate the effect of two different deployment schemes of UAVs on learning and network performance. Last but not least, we highlight several technical challenges and future research directions in AGIFL.

中文翻译：

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空地一体化联邦学习赋能边缘智能


泛在智能已被广泛认为是未来第六代（6G）网络的关键愿景，这意味着从核心到边缘（包括终端设备）的整个网络的智能。然而，由于边缘设备的资源有限以及6G所设想的无处不在的覆盖，实现这一愿景，特别是边缘的智能，极具挑战性。为了赋能边缘智能，在本文中，我们提出了一种称为空地集成联邦学习（AGIFL）的框架，它将空地集成网络和联邦学习（FL）有机地集成在一起。在AGIFL中，利用无人机等空中节点灵活的按需3D部署，所有节点都可以通过FL协同训练有效的学习模型。我们还进行了案例研究来评估两种不同的无人机部署方案对学习和网络性能的影响。最后但并非最不重要的一点是，我们强调了 AGIFL 的几个技术挑战和未来的研究方向。