Low-overhead Clustered Federated Learning for Personalized Stress Monitoring

Advances in Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) are enabling remote health monitoring in everyday settings for early detection and prevention of chronic health problems. Such solutions can be used to augment a conventional physician-centered healthcare system. Stress, as one of the critical health problems, affects individuals adversely in terms of both physical and mental health. Prior studies on stress evaluation utilize a centralized cloud-based approach that combines data from each client for modeling. However, such a centralized approach raises data privacy concerns. To preserve privacy, decentralized federated learning has been proposed as a potential alternative framework. Nevertheless, existing federated learning algorithms have to deal with data heterogeneity; data skewness in each participant can significantly degrade the overall model performance. To tackle this challenge, we present a personalized, low-overhead clustered federated learning algorithm for stress-level recognition. The proposed algorithm outperforms two state-of-the-art baseline algorithms by providing over 7% and 12% increase in accuracy, respectively. The proposed algorithm also obtains a reduction of 37.5% and 9.6% in the training runtime compared to the two baseline algorithms. We also present a novel cold-start algorithm for new clients who join the trained system. Our results suggest that this cold-start algorithm is robust in terms of individual classification accuracy and total training time.