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.

中文翻译：

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通过可重构智能表面实现智能物联网的联合机器学习


随着人工智能和高维数据分析的进步，智能物联网（IoT）将发生变革，从“物联”向“智联”转变。这将在自动驾驶汽车、无人机、医疗保健、机器人和供应链金融等众多令人兴奋的应用中释放智能物联网的全部潜力。这些应用程序推动了开发革命性计算、通信和人工智能技术的需求，这些技术可以利用大量实时数据做出低延迟决策。为此，联邦机器学习作为一种颠覆性技术应运而生，它可以从网络边缘的数据中提取智能，同时保证设备隐私和数据安全。然而，有限的通信带宽是通过无线电信道进行联合机器学习的模型聚合的关键瓶颈。在本文中，我们将通过利用多路访问信道的波形叠加特性，为智能物联网网络开发一种基于无线计算的通信高效联合机器学习框架。通过重新配置无线传播环境来增强信号强度，进一步利用可重构智能表面来减少模型聚合误差。