"ArgusEye is a privacy-preserving Intrusion Detection System (IDS) designed to detect network anomalies and attacks without compromising user data. Traditional IDS solutions typically rely on centralized machine learning models that require the collection of raw data from client nodes. While effective, this centralized approach raises serious privacy concerns, increases the risk of data breaches, and potentially violates compliance requirements in sensitive domains such as healthcare and finance. ArgusEye addresses these limitations through the integration of Federated Learning (FL) and Differential Privacy (DP). Federated Learning enables decentralized model training by allowing each client device to train on its local data, sharing only model updates, and not the data itself, with a central server. To enhance privacy further, Differential Privacy is applied by adding noise to the aggregated updates, making it difficult to infer any single user’s contribution. The system was developed and evaluated using a widely used benchmark dataset and was designed to simulate multiple clients operating in a federated learning environment. Experiments were conducted to study the impact of differential privacy on model performance and training time. ArgusEye achieved strong detection metrics, which demonstrate that federated learning, when combined with privacy-preserving technologies, can offer a practical and effective solution for intrusion detection in privacy-sensitive environments."