Abstract: In recent years, the rapid urbanization and development of the social economy have led to a growing focus on public safety issues. Governments across the world are increasingly promoting the construction of smart cities and intelligent security systems to safeguard the lives and property of citizens and maintain social stability. Person re-identification (ReID) is an essential technology for building smart cities, with significant implications for security monitoring and criminal investigation applications. The goal of person re-identification is to accurately identify specific individuals captured under different cameras. However, due to intra-class differences resulting from various factors such as illumination, viewpoint, occlusion, and pose, person re-identification remains a challenging task in the field of computer vision. Although existing fully supervised person re-identification methods have made significant progress, the scarcity of data and labels poses a bottleneck to further improving model performance. To address this challenge, this paper introduces a more complex and diverse synthetic dataset with easy-to-obtain labels for auxiliary training, and proposes a novel Camera-aware Asymmetric Adversarial Learning (CAAL) method that overcomes intra-class variation among multiple cameras and the domain-shift between real and synthetic data, enabling the learning of camera-invariant feature representations from diverse data sources. Furthermore, to mitigate the impact of misleading information carried by synthetic datasets and prevent the model from overfitting to synthetic data during adversarial training, we propose using an auxiliary network trained on real-world data to constrain the training of the backbone network. We conduct extensive experiments on two public datasets to demonstrate the effectiveness of the proposed method.