Univrsity Of Tehran Press Journal of Information Technology Management 2980-7972 18 4 2026 10 01 Explainable Privacy-Preserving Federated Learning for Collaborative Gout Risk Management Using Genetic and Lifestyle Data 93 116 108917 10.22059/jitm.2026.108917 EN Yu-Ruey Liu College of Information and Electrical Engineering, Asia University, Taichung, Taiwan; Department of Emergency Medicine, Cheng Ching General Hospital, Taichung, Taiwan. Chun-Yuan Lin Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan. Yo-Yu Liu Department of Business Administration, Nanhua University, Chiayi 622, Taiwan. Sneha Patnaik School of Public Health, KIIT-DU, Bhubaneswar, India. Khemraj Sharma School of Management, Kalinga Institute of Industrial Technology, Bhubaneswar, India. Journal Article 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. Federated Learning Gout Risk Prediction Privacy-Preserving Machine Learning Communication-Efficient Learning Differential Privacy Secure Aggregation https://jitm.ut.ac.ir/article_108917_1f2c4c4aa2d36dd49a4cee8f253c0a9e.pdf