通过空地一体化联邦学习赋能边缘智能
Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning
AI总结：
针对6G边缘智能面临的资源与覆盖挑战，提出空地一体化联邦学习框架AGIFL，利用UAV按需3D部署协同训练模型，评估了不同部署方案的影响并探讨了未来研究方向。
摘要：
泛在智能已被广泛认为是未来第六代（6G）网络的关键愿景，这意味着覆盖从核心到边缘（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所设想的泛在覆盖，实现这一愿景，特别是边缘智能，极具挑战性。为了赋能边缘智能，在本文中，我们提出了一种名为AGIFL（空地一体化联邦学习）的新颖框架，该框架将空地一体化网络与联邦学习（FL）有机结合。在AGIFL中，利用无人飞行器（UAV）等空中节点灵活的按需3D部署，所有节点均可通过FL协同训练有效的学习模型。我们还进行了案例研究，以评估UAV的两种不同部署方案对学习性能和网络性能的影响。最后，我们强调了AGIFL中的几个技术挑战和未来研究方向。
英文摘要：
Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices. Nevertheless, fulfilling such 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 the edge intelligence, in this article, we propose a novel framework called AGIFL (Air-Ground Integrated Federated Learning), which organically integrates air-ground integrated networks and federated learning (FL). In the 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 the UAV over the learning and network performance. Last but not the least, we highlight several technical challenges and future research directions in the AGIFL.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
