Abstract: Intrusion detection system (IDS) is a pivotal component in ensuring the security of network environments. However, conventional deep learning methodologies are characterised by elevated false alarm rates when confronted with infrequent attack features and challenges related to data imbalance. Additionally, these methodologies possess a limited capacity to effectively model high-dimensional sparse features. The ubiquity of the Internet of things (IoT) and edge devices has led to the emergence of intrusion detection data that exhibits distributed and heterogeneous trends. The centralised training of this data is hindered by privacy protection requirements, necessitating alternative approaches for data analysis and interpretation. In this regard, we put forward a federated classical hybrid quantum convolutional intrusion detection system (FedCQIDS), which involves the incorporation of a lightweight quantum convolutional neural network within the client-side local model. The convolutional layer is incorporated into the client-local model with the objective of enhancing resource utilisation through quantum bit multiplexing, augmenting the capacity to express low-frequency sparse attack features, and facilitating multi-client collaborative modelling and privacy protection by means of federated learning architecture. A series of related experiments are conducted on two distinct datasets, NSL-KDD and UNSW-NB15, and the findings indicate that FedCQIDS exhibits superior performance compared with conventional federated models with regard to accuracy and loss value. This is particularly evident in scenarios involving the identification of a limited number of classes of attacks, thereby underscoring its potential for application in the domain of privacy protection and efficient deployment.