Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface

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...

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Veröffentlicht in:IEEE network 2020-09, Vol.34 (5), p.16-22
Hauptverfasser: Yang, Kai, Shi, Yuanming, Zhou, Yong, Yang, Zhanpeng, Fu, Liqun, Chen, Wei
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:0890-8044
1558-156X
DOI:10.1109/MNET.011.2000045