论文标题
关于空的边缘学习的概述
An Overview on Over-the-Air Federated Edge Learning
论文作者
论文摘要
空中联邦边缘学习（Air-Feel）已成为支持边缘人工智能（AI）的有前途的解决方案，将来超过5G（B5G）和6G网络。在空中分布式边缘设备中，使用其本地数据来协作训练AI模型，同时保留数据隐私，其中利用了空中模型/梯度聚合来提高学习效率。本文概述了有关风险的现状。首先，我们介绍了空气摩托的基本原理，并由于空中聚合错误以及边缘设备的资源和数据异质性引起的气动设计挑战。接下来，我们介绍了空气赛车的基本性能指标，并审查资源管理解决方案和设计注意事项，以提高气动性能。最后，指出了一些有趣的研究指示，以激发未来的工作。
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.
