Privacy-Preserving Collaborative Anomaly Detection in Wearable Health Data Using Federated Learning

The wearables market has developed into an industry which now enables people to track their vital signs throughout the day thus helping medical professionals to detect serious conditions, such as arrhythmias, and stress-related heart problems, from their vital signs. Nevertheless, distributed anomaly detection in Internet of Things (IoT) environments comes with a level of privacy issues because these solutions still need to aggregate users' sensitive data. This paper focuses on the issue of privacy-preserved collaborative anomaly detection in the wearable health data with federated learning (FL). FL enables decentralized training on a distributed set of devices while keeping the raw data local, but still contributing to a global model. It provides a detailed review on federated anomaly detection techniques that employ model architectures, such as autoencoders, LSTM and Convolutional Neural Network (CNNs) in supervised as well as unsupervised manner. If leverage privacy-enhancing tools such as differential privacy and secure aggregation to mitigate any inference risk associated with the sharing of gradients. Datasets available in the public domain ranging from WESAD, WISDM, MIT-BIH Arrhythmia are recognized as appropriate benchmarks for a FL-based anomaly detection task aimed for healthcare. the results demonstrate that federated models can achieve centralized performance for anomaly detection while also offering a large boost in data privacy. The combination of hierarchical and personalized FL methods brings both improved system scalability and better detection performance in heterogeneous operational environments. The research identifies critical questions about model customization and immediate system modifications and combined data analysis from various sources. The research establishes an organized framework which designers can use to create robust privacy-protecting anomaly detection systems that operate in wearable health technology environments used for clinical monitoring and mHealth applications and secure Internet of Medical Things (IoMT) environments. The method obtained 94% AUC-ROC and 0.89 F1-score results which matched the performance of centralized systems while maintaining user privacy.