In the realm of intrusion detection, the burgeoning complexity of cyber threats necessitates innovative approaches to fortify network security. Federated learning (FL) is proposed to enhance intrusion detection systems (IDS) in Internet of Things (IoT) networks without compromising privacy. In this work, data is divided into silos, each having a trainer assigned to it, in cross-silo FL. The proposed model centralizes the coordination of the learning process while retaining the decentralized nature of data storage. IoT devices across the network contribute local knowledge to a central server, which orchestrates model training and updates. This approach enables a seamless amalgamation of insights, fostering a more comprehensive understanding of emerging threats and anomalies within the network. The research delves into designing and implementing a cross-silo FL framework for intrusion detection, focusing on optimizing communication protocols, minimizing latency, and ensuring data privacy compliance. To provide exposure to a wide variety of pertinent features and scenarios, the study provides a robust FL environment. It makes use of a diversified dataset taken from the IoTID20 dataset. Training the model on a local system and a local area network (LAN) shows flexibility. The model’s efficiency is demonstrated in real-world deployment settings, predicting anomalies with an astounding accuracy rate of up to 98.92%.