NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data
This addresses the problem of evaluating federated learning models under realistic non-IID conditions for researchers in IoT and smart city applications, though it is incremental as it focuses on dataset creation.
The authors tackled the lack of public datasets for studying non-IID data in federated learning for person detection by releasing NIPD, a real-world dataset from five cameras, and provided a benchmark, showing it is the first such device-based dataset.
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.