Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

doi:10.1109/MNET.011.2000045

	Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
	Kai Yang1 ; Yuanming Shi1 ; Yong Zhou1 ; Zhanpeng Yang1 ; Liqun Fu 2; Wei Chen 3
	2020-09
发表期刊	IEEE NETWORK (IF:6.8[JCR-2023],8.5[5-Year])
ISSN	0890-8044
卷号	34 期号:5 页码:16-22
发表状态	已发表
DOI	10.1109/MNET.011.2000045
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.
关键词	Internet of Things Machine learning Computational modeling Servers Data models Atmospheric modeling
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收录类别	SCI ; SCIE ; EI
语种	英语
资助项目	National Key Research and Development Program[2018YFA0701601] ; National Natural Science Foundation of China (NSFC)[61601290][61971286][61771017]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000591303900003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
来源库	IEEE
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/119203
专题	信息科学与技术学院_博士生信息科学与技术学院_PI研究组_石远明组信息科学与技术学院_PI研究组_周勇组
作者单位	1.ShanghaiTech University, Shanghai, China 2.Xiamen University, Xiamen, China 3.Tsinghua University, Beijing, China
第一作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Kai Yang,Yuanming Shi,Yong Zhou,et al. Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface[J]. IEEE NETWORK,2020,34(5):16-22.
APA	Kai Yang,Yuanming Shi,Yong Zhou,Zhanpeng Yang,Liqun Fu,&Wei Chen.(2020).Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface.IEEE NETWORK,34(5),16-22.
MLA	Kai Yang,et al."Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface".IEEE NETWORK 34.5(2020):16-22.