This paper addresses the challenges of anomaly detection in dynamic environments characterized by extensive real-time data and privacy concerns. A novel framework integrating Federated Learning (FL) and Digital Twin (DT) technologies is proposed. FL facilitates decentralized machine learning, maintaining data privacy, while DT provides virtual replicas for real-time system monitoring. The framework enhances anomaly detection rates, interpretability, and responsiveness, offering a robust solution for sectors like healthcare and manufacturing. Using autoencoders for threshold-based anomaly identification and hybrid classifiers for anomaly classification, the approach achieves superior detection accuracy and computational efficiency. Results demonstrate the model's effectiveness in safeguarding systems from operational disruptions.