Personalized Nutrition Recommendation System Based on Artificial Intelligence and Federated Learning

This study developed a personalized nutrition recommendation system based on federated learning and multiple sources of health data, including clinical records, genetic information, lifestyle surveys, and physiological data from wearable devices. The system was designed to provide individual dietary advice while protecting user privacy. A six-month controlled study compared the outcomes of a personalized intervention group with those of a control group following standard dietary guidelines. Results showed that the personalized group achieved a 15% increase in dietary quality scores, along with better health outcomes. These included an average weight loss of 3.0kg, a 2.0% reduction in body fat, and higher rates of normal blood pressure and stable blood glucose levels. In addition, 80% of participants in the personalized group reported higher satisfaction, noting that the recommendations matched their preferences and were easier to follow. The system also included methods to explain how each recommendation was generated, helping users and health professionals better understand and trust the results. Overall, this approach shows promise for improving nutrition management and supporting long-term health.