In the big data era, there has been an increasing consensus that the label information, computational resources and communication bandwidth are particularly precious. State-of-the-art research is revolutionizing the vision systems of the smart city, which converts the visual signals from sensory input into feature representations and conveys the compact feature for analysis by using the computational resources of both front and back ends. To deploy a robust model, large amounts of labeled data are usually required, and thereby heavy computational and communication resources are incurred in model training as well as inference. However, the computational resources in front-end devices are usually constrained, and heavy transmission burden is imposed when leveraging multiple models amongst different ends. In this work, we propose a novel collaborative computing approach for intelligent sensing and low-cost analysis, which reduces the requirement of labeled data and communication cost, and balances the computational load in model training and inference. By incorporating the adversarial learning mechanism into collaborative model training, knowledge of different domains can be better exploited. Moreover, the learned models are deployed for inference in a collaborative manner, in which part of model is placed in front-ends for extracting intermediate feature maps, and part of the model remains in back ends for inference with received feature maps. The effectiveness of the proposed approach has been validated in the context of an emerging digital retina system for smart city intelligent applications.