Federated Learning based Diet Recommendation System

In contemporary society, numerous individuals face a wide array of health issues and concerns. Advising an appropriate diet is frequently difficult owing to the diverse needs, including weight loss, weight gain, and overall health maintenance, coupled with the constraints of time. To address this challenge, we embarked on developing a program aimed at promoting healthiereating habits. Our approach focuses on recommending only three categories of goods: those conducive to weight loss, weight gain, and maintaining general well-being. Our System of Dietary Recommendations relies on a comprehensive nutrient database, encompassing precise information about various nutrients. To tailor dietary suggestions, the system considers user inputs such as medical data and dietary preferences, including the choice between vegetarian and non- vegetarian meals within the aforementioned categories. In this discussion, we delve into the realms of food classification, essential parameters, and the application of machine learning techniques. The Federated Food Classification project encompasses a secure system with userfriendly features. Key modules include a secure login, anintuitive interface, a robust food classification using Convolutional Neural Networks with a 96.97% accuracy, a health-oriented BMI calculator, and personalized Indian food recommendations. The project successfully combines technological prowess in image classification and data analysis, addressing practical health concerns and cultural dimensions. Future enhancements aim to broaden capabilities, emphasizing the project's potential in innovative food-related applications. health applications and cultural dimensions, presenting a holistic solution for users.