Hierarchical Personalized Continual Federated Learning for Real Time Risk Prediction of Chronic Diseases

The most prevalent global morbidity and mortality is chronic illnesses like cardiovascular diseases, diabetes, and respiratory diseases which require the proper prediction of risks and in a timely manner so as to have preventative measures. Nevertheless, predictive systems in real-time have been found to be severely limited by fragmented healthcare data, patient population heterogeneity, non-independent and identically distributed (non-IID) data distributions, and strict privacy policies that cannot allow direct data sharing. Current centralized systems tend to perform poorly when it comes to generalizing across dissimilar healthcare locations, and traditional federated learning algorithms have a scalability bottleneck, suboptimal communication, inadequate personalization, and susceptibility to data drift with time, rendering them unsuitable to real-world application. To overcome these obstacles, we suggest a Hierarchical Personalized Continual Federated Learning (HiPerC-FL) model of real-time risk prediction of chronic diseases that incorporates multi-modal input of wearable, electronic health records, and imaging data without having to reveal raw patient data. The system uses a hierarchical aggregation topology between edge devices, hospital servers, and global coordinators to reduce the latency and communication and a personalized meta-learning module coupled with client clustering helps to address the impact of data heterogeneity. Moreover, on-device adaptation that happens continuously allows local models to be immune to concept drift, and a causal feature regularizer makes predictions more interpretable and reliable. Secure aggregation, differential privacy, and verifiable audit trails are the means of implementing privacy and governance, and both adhere to clinical standards. Benchmark healthcare simulation Experimental results on benchmark healthcare data show that, compared to baseline federated methods, HiPerC-FL always yields progress of 7–10 percent in predictive accuracy, is 50 percent more cost-effective in communication, and GUI remains stable under extended distribution shifts. This evidence confirms that the given framework proves to be not only effective but also practically deployable to the real-time chronic disease monitoring process, which can serve as a scalable and ethically-acceptable roadmap to the precision of the healthcare provision.