DRFAS-enhanced personalized federated learning for secure and robust cardiovascular disease detection

The integration of privacy protection in smart healthcare systems has revolutionized disease detection and patient monitoring, but privacy concerns arise from the collection and analysis of sensitive health data. Federated learning (FL) offers data privacy to diverse clients, but a single global model fails. Personalized FL addresses privacy concerns, focusing on individual needs. The proposed differential privacy personalized federated learning with a deep learning model (Per-FLDL), along with Distributed Robust Federated Aggregation Service (DRFAS) techniques, accurately detects, predicts, and improves correlation understanding of cardiovascular disease in smart healthcare systems, thereby allowing the clients to design their own privacy-preserving personalized FL models. The proposed system minimizes data exposure, enhances local model training, and improves personalization and illness detection accuracy by utilizing the FL approach. By utilizing federated learning, decentralized model training is enabled while guaranteeing that personal health data remains on-device, reducing dangers associated with data breaches and illegal access. The proposed Per-FLDL framework achieved high accuracy rates of 98.79%, 98.05%, and 99.32% in healthcare, heart disease, and cardiovascular disease datasets, respectively.