Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866912091307245568 |
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| author | Yang, Kai Shi, Yuanming Zhou, Yong Yang, Zhanpeng Fu, Liqun Chen, Wei |
| author_facet | Yang, Kai Shi, Yuanming Zhou, Yong Yang, Zhanpeng Fu, Liqun Chen, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_05843 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface Yang, Kai Shi, Yuanming Zhou, Yong Yang, Zhanpeng Fu, Liqun Chen, Wei Signal Processing Machine Learning Networking and Internet Architecture 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. |
| title | Federated Machine Learning for Intelligent IoT via Reconfigurable Intelligent Surface |
| topic | Signal Processing Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2004.05843 |