  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.
