Federated Learning (FL) allows multiple devices to collaboratively train a model without transferring sensitive data, which is particularly important in the context of Intrusion Detection Systems (IDS) due to the high privacy concern. FL preserves privacy and complies with data protection legislation by keeping the data local. This adds to the improved scalability and collaboration intelligence across the devices, to improve the real-time threat detection. The following section provides a comprehensive review of studies that fit this description and are published in 2023–2024, We process the used datasets, the adopted learning mechanisms, scalability of FL models, and different aggregation functions that aid in combining models updates with consistency and high accuracy. This review provides valuable insights into the state of integrating FL in the IDS domain, including the latest trends and challenges. It examines the objectives of various IDS projects, the characteristics of existing datasets, the aggregation functions used, and the machine learning (ML) and deep learning (DL) techniques applied, along with their performance.