Unknown object detection aims to build detectors capable of identifying out-of-distribution objects, a critical need for applications like autonomous driving and traffic monitoring. However, limited device resources restrict existing methods from achieving accurate detection on the device side. Addressing this gap, this paper introduces a device-cloud collaborative framework named DCCUOD that enhances device model performance through efficient cloud collaboration. Our framework employs an energy-based sampling function on devices to target samples with unknown objects, coupled with a collaborative pseudo-labeling strategy to generate accurate pseudo-labels. Additionally, a two-stage training paradigm enables continuous improvements of device models on both known and unknown objects. Our study is the first to explore device-cloud collaborative learning for UOD tasks. Experimental results show that the device model is three times smaller and seven times faster than cloud models, with minimal performance trade-offs.