Explainable Privacy-Preserving Federated Learning for Collaborative Gout Risk Management Using Genetic and Lifestyle Data

Beyond improving predictive performance, the proposed framework contributes to healthcare information management by enabling secure collaboration among geographically distributed healthcare institutions while complying with data privacy regulations. The explainable prediction mechanism enhances clinicians' confidence in AI-assisted decision-making by identifying key genetic and lifestyle risk factors that influence preventive interventions. The proposed framework offers practical value for hospital administrators, healthcare policymakers, and digital health managers seeking scalable, privacy-preserving, and interpretable AI solutions for collaborative clinical analytics and precision healthcare.

کلیدواژه ها:

Federated Learning ، Gout Risk Prediction ، Privacy-Preserving Machine Learning ، Communication-Efficient Learning ، Differential Privacy ، Secure Aggregation