An Overview on Over-the-Air Federated Edge Learning

Xiaowen Cao; Zhonghao Lyu; Guangxu Zhu; Jie Xu; Lexi Xu; Shuguang Cui

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arXiv · 2208.05643

An Overview on Over-the-Air Federated Edge Learning

Xiaowen Cao·Zhonghao Lyu·Guangxu Zhu·Jie Xu·Lexi Xu·Shuguang Cui

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Abstract

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.

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Xiaowen Cao, Zhonghao Lyu, Guangxu Zhu, Jie Xu, Lexi Xu, Shuguang Cui. 2022-08-11. An Overview on Over-the-Air Federated Edge Learning. https://arxiv.org/abs/2208.05643

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