Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

Autor:	Yang, Kai, Shi, Yuanming, Zhou, Yong, Yang, Zhanpeng, Fu, Liqun, Chen, Wei
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Machine Learning Computer Science - Networking and Internet Architecture
Druh dokumentu:	Working Paper
Popis:	Intelligent Internet-of-Things (IoT) will be transformative with the advancement of artificial intelligence and high-dimensional data analysis, shifting from "connected things" to "connected intelligence". This shall unleash the full potential of intelligent IoT in a plethora of exciting applications, such as self-driving cars, unmanned aerial vehicles, healthcare, robotics, and supply chain finance. These applications drive the need of developing revolutionary computation, communication and artificial intelligence technologies that can make low-latency decisions with massive real-time data. To this end, federated machine learning, as a disruptive technology, is emerged to distill intelligence from the data at network edge, while guaranteeing device privacy and data security. However, the limited communication bandwidth is a key bottleneck of model aggregation for federated machine learning over radio channels. In this article, we shall develop an over-the-air computation based communication-efficient federated machine learning framework for intelligent IoT networks via exploiting the waveform superposition property of a multi-access channel. Reconfigurable intelligent surface is further leveraged to reduce the model aggregation error via enhancing the signal strength by reconfiguring the wireless propagation environments. Comment: This work has been accepted by the IEEE Network Magazine for publication
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2004.05843 Zobrazit plný text záznamu View this record from Arxiv