Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing power if combined with FL to process the video data directly at the edge. However, due to the different hardware and deployment scenarios of different cameras, the data collected by the camera present non-independent and identically distributed (non-IID), and the global model derived from FL aggregation is less effective. Meanwhile, existing research lacks public data set for real-world FL object detection, which is not conducive to studying the non-IID problem on IoT cameras. Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras. To our knowledge, this is the first true device-based non-IID person detection data set. Based on this data set, we explain how to establish a FL experimental platform and provide a benchmark for non-IID person detection. NIPD is expected to promote the application of FL and the security of smart city.

翻译：联邦学习（FL）作为一种保护隐私的分布式机器学习方法，已快速应用于无线通信网络。FL使得物联网（IoT）客户端能够在防止隐私泄露的同时获得训练良好的模型。若与FL相结合，行人检测可直接在边缘设备上处理视频数据，从而部署于计算能力有限的边缘设备。然而，由于不同摄像头的硬件配置和部署场景存在差异，摄像头采集的数据呈现非独立同分布（non-IID）特性，导致通过FL聚合得到的全局模型效果不佳。同时，现有研究缺乏面向真实世界FL目标检测的公开数据集，这不利于研究物联网摄像头上的非独立同分布问题。为此，我们开源了一个非独立同分布物联网行人检测（NIPD）数据集，该数据集采集自五台不同的摄像头。据我们所知，这是首个基于真实设备的非独立同分布行人检测数据集。基于该数据集，我们阐释了如何构建FL实验平台，并提供了非独立同分布行人检测的基准。NIPD有望推动FL的应用以及智慧城市的安全性提升。