In cross-domain Internet of Things (IoT) networks, the centralized intrusion detection system faces the challenge of privacy security and performance bottlenecks during centralized data processing. Distributed federated learning frameworks enable the training of models without sharing private data, thereby providing a highly scalable, privacy-friendly solution for IoT intrusion detection. However, federated learning also faces the challenge of label distribution skew caused by non-independent and identically distributed (non-IID) data, as well as client selection and aggregation efficiency. To address the above challenge, we propose an enhanced IoT intrusion detection method based on contrastive federated learning, i.e., ID-CFL. Firstly, the introduction of the federated learning framework alleviates privacy concerns as users do not need to share private data. Secondly, we design an intrusion detection method based on contrastive federated learning. It achieves abnormal detection by analyzing the inherent feature similarity among data, reducing the reliance on explicit label information, and demonstrating improved robustness in scenes with label distribution skew. Lastly, we propose an efficient federated aggregation algorithm based on node correlation degree. Extensive experiments on three well-known datasets demonstrate that the ID-CFL model exhibits outstanding performance in accuracy and communication efficiency.