Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
Kai Yang, Yuanming Shi, Yong Zhou, Zhanpeng Yang, Liqun Fu, Wei Chen
TL;DR
An overthe- air computation-based communication-efficient federated machine learning framework for intelligent IoT networks is developed via exploiting the waveform superposition property of a multi-access channel and reconfiguring the wireless propagation environments.
Abstract
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.