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A Distributed Model for IoT Anomaly Detection Using Federated Learning
Abstract
Anomaly detection in IoT-based sleep patterns is crucial for early identification of health issues. This chapter presents a distributed model using federated learning for privacy and data security. The proposed approach involves data collection, preprocessing, model initialization, federated learning server, model distribution, and anomaly detection. Sleep pattern data is preprocessed to extract features, initializing the global anomaly detection model. A federated learning server enables collaborative learning with devices, distributing the updated global model iteratively for synchronized anomaly detection. Precision and accuracy metrics yielded 0.67% precision and 0.84% accuracy, showcasing the effectiveness of the distributed model. Leveraging federated learning ensures privacy, data security, and synchronized anomaly detection across devices, supporting early detection of sleep-related anomalies and health interventions.
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