Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

被引:131
作者
Yang, Kai [1 ]
Shi, Yuanming [1 ]
Zhou, Yong [1 ]
Yang, Zhanpeng [1 ]
Fu, Liqun [2 ]
Chen, Wei [3 ]
机构
[1] ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai, Peoples R China
[2] Xiamen Univ, Sch Informat, Xiamen, Peoples R China
[3] Tsinghua Univ, Dept Elect Engn, Beijing, Peoples R China
来源
IEEE NETWORK | 2020年 / 34卷 / 05期
基金
中国国家自然科学基金;
关键词
Internet of Things; Machine learning; Computational modeling; Servers; Data models; Atmospheric modeling; INTERNET;
D O I
10.1109/MNET.011.2000045
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
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 to develop revolutionary computation, communication, and artificial intelligence technologies that can make low-latency decisions with massive realtime data. To this end, federated machine learning, as a disruptive technology, has emerged to distill intelligence from the data at the 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 overthe- 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.
引用
收藏
页码:16 / 22
页数:7
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