Predicting future heart rate (HR) not only helps in detecting abnormal heart rhythms but also provides timely support for downstream health monitoring services. Existing methods for HR prediction encounter challenges, especially concerning privacy protection and data heterogeneity. To address these challenges, this paper proposes a novel HR prediction framework, PCFedH, which leverages personalized federated learning and prototypical contrastive learning to achieve stable clustering results and more accurate predictions. PCFedH contains two core modules: a prototypical contrastive learning-based federated clustering module, which characterizes data heterogeneity and enhances HR representation to facilitate more effective clustering, and a two-phase soft clustered federated learning module, which enables personalized performance improvements for each local model based on stable clustering results. Experimental results on two real-world datasets demonstrate the superiority of our approach over state-of-the-art methods, achieving an average reduction of 3.1% in the mean squared error across both datasets. Additionally, we conduct comprehensive experiments to empirically validate the effectiveness of the key components in the proposed method. Among these, the personalization component is identified as the most crucial aspect of our design, indicating its substantial impact on overall performance.

The prediction of health metrics for drivers has become increasingly crucial due to the potential impact of drivers' health conditions on traffic accidents. Heart attack is one of the primary causes of health-related traffic tragedies. However, drivers' heart rate (HR) is considered highly-private data, which should not be collected by a centralized server for training the prediction model. To this end, we contribute FedHeart, a novel privacy-preserving federated learning (FL) system for HR prediction. We observe distinct HR changes when drivers are in steady-state and changing-state conditions, and thus we utilize FL to train two separate models for these states. To enhance the prediction accuracy, we incorporate contrastive learning to extract HR features. Through experiments on two real-world datasets, we validate the efficiency of the proposed system in accurately predicting HR during driving scenarios.