This paper proposes an Intrusion Detection System (IDS) that utilizes Ensemble Models and Federated Learning with Differential Privacy for robust and efficient intrusion detection while ensuring data privacy. The Ensemble Models improve IDS performance by leveraging multiple machine learning models. Federated Learning allows local model training on each network node, preserving data privacy and enabling scalability. Differential Privacy further enhances privacy by preventing data-specific information revelation in node updates. This innovative approach could set a new standard in network security, offering a scalable, adaptable, and privacy-preserving solution for intrusion detection, contributing significantly to machine learning, network security, and privacy-preserving technology research.