Over-the-air federated edge learning (Air-FEEL) has emerged as a promising solution to support edge artificial intelligence (AI) in future beyond 5G (B5G) and 6G networks. In Air-FEEL, distributed edge devices use their local data to collaboratively train AI models while preserving data privacy, in which the over-the-air model/gradient aggregation is exploited for enhancing the learning efficiency. This article provides an overview on the state of the art of Air-FEEL. First, we present the basic principle of Air-FEEL, and introduce the technical challenges for Air-FEEL design due to the over-the-air aggregation errors, as well as the resource and data heterogeneities at edge devices. Next, we present the fundamental performance metrics for Air-FEEL, and review resource management solutions and design considerations for enhancing the Air-FEEL performance. Finally, several interesting research directions are pointed out to motivate future work.
翻译:空中联合边缘学习(Air-FEEL)已经成为支持未来超过5G(B5G)和6G网络的边缘人工智能(AI)的一个大有希望的解决办法。在空中-FEEL中,分布边缘装置利用当地数据合作培训AI模型,同时保护数据隐私,其中利用空中联合模型/梯级汇总提高学习效率。这一条概述了Air-FEEL的先进水平。首先,我们提出了空气-FEEL的基本原则,并介绍了空中联合错误给空气-FEEL设计带来的技术挑战,以及边缘装置的资源和数据差异。接下来,我们介绍了空气-FEEL的基本性能衡量标准,并审查了加强空中-FEEL绩效的资源管理解决方案和设计考虑。最后,我们指出了若干有趣的研究方向,以激励今后的工作。