<p>Intrusion detection systems (IDS) are crucial in the identification of unauthorized activities on a digital network, enabling cybersecurity measures to initiate prevention protocols to protect the security of their networks and data. With Internet of Things (IoT) as a ubiquitous phenomenon, IDS has become increasingly important owing to the massive growth of connected devices. Machine learning algorithms can detect anomalies in large datasets, but centralized methods pose security risks by gathering all data in one place. Federated learning (FL) offers a safer alternative, allowing multiple clients to collaborate on a model without sharing data, reducing the risk of data leaks and enhancing privacy. In this paper, we propose a FL-based IDS. Herein, we have applied the FL approach to a number of clients that are grouped in sets where the number of clients in a set varies from 2 to 10. After that, we applied feature ranking via a random forest approach, wherein the features are ranked based on their importance to find the optimal and reduced dataset for reducing the inference time. Further, three models for each of the clients were trained using the refined dataset obtained. Next, the central server aggregates the data from the clients using three methods. We have updated the dataset with two types of scenarios, one with two classes (Benign and Attacks) and the second with a multi-class (Benign, Dodag, Flooding, Rank, Blackhole) for identifying the attack. The experimental results establish that the proposed methodology achieves superior performance as compared to similar existing methodologies.</p>