This paper presents an automatic method for the design of Neural Networks (NNs) at the edge, enabling Machine Learning (ML) access even in privacy-sensitive Internet of Things (IoT) applications. The proposed method runs on IoT gateways and designs NNs for connected sensor nodes without sharing the collected data outside the local network, keeping the data in the site of collection. This approach has the potential to enable ML for Healthcare Internet of Things (HIoT) and Industrial Internet of Things (IIoT), designing hardware-friendly and custom NNs at the edge for personalized healthcare and advanced industrial services such as quality control, predictive maintenance, or fault diagnosis. By preventing data from being disclosed to cloud services, this method safeguards sensitive information, including industrial secrets and personal data. The outcomes of a thorough experimental session confirm that -- on the Visual Wake Words dataset -- the proposed approach can achieve state-of-the-art results by exploiting a search procedure that runs in less than 10 hours on the Raspberry Pi Zero 2.

翻译：本文提出了一种在边缘端自动设计神经网络的方法，使得即使在隐私敏感的物联网应用中也能实现机器学习。该方法在物联网网关上运行，为连接的传感器节点设计神经网络，无需将收集的数据共享到本地网络之外，从而将数据保留在采集现场。该技术有望推动医疗物联网和工业物联网中的机器学习应用，在边缘端设计硬件友好且定制化的神经网络，用于个性化医疗及先进工业服务（如质量控制、预测性维护或故障诊断）。通过防止数据泄露至云服务，该方法保障了工业机密与个人数据等敏感信息的安全性。详尽的实验结果表明——在Visual Wake Words数据集上——该方法利用在树莓派Zero 2上运行时间不超过10小时的搜索流程，即可取得与当前最优方法相媲美的结果。