A Federated Deep Learning Model for Privacy-Aware Healthcare Analytics and Personalized Disease Prediction

Abstract

The fast rise of artificial intelligence-based healthcare analytics has greatly enhanced the process of disease diagnostics, patient monitoring, and medical decision-making that is personalized. Nevertheless, traditional centralized healthcare models usually have significant issues concerning the privacy of patient data, data vulnerability to security threats, and also limited data exchange within healthcare facilities. These threats work against effective use of distributed datasets of healthcare to give accurate disease prediction and smart clinical analysis. In order to overcome these drawbacks, the study will introduce a federated deep learning architecture of privacy-conscious healthcare analytics and personalized disease prediction. The suggested framework can be used by various healthcare facilities to cooperatively train deep learning models without sharing sensitive patient data, which will retain the confidentiality of the data and improve safe medical analytics. The methodology is the combination of federated learning with deep neural network-based disease prediction algorithms and privacy-sensitive communication techniques to secure the aggregate of parameters and distributed model optimization. The local model training and global federated aggregation is carried out on healthcare datasets that are preprocessed in terms of normalization, feature extraction, and data balancing. The framework is tested based on the disease prediction performance metrics such as accuracy, precision, recall, specificity, F1-score, and ROC-AUC analysis. The experimental findings prove that the proposed federated structure yields enhanced accuracy in disease prediction, personalized healthcare analytics, decreased privacy risk and an efficient collaborative learning behavior in contrast to the conventional centralized methods. The proposed model helps to build the safe, scalable, and intelligent healthcare systems in the next generation of privacy-aware medical analytics applications.