Diabetes Prediction using Federated Learning

Federated learning is an innovative approach used in the medical field to resolve issues like centralization, privacy, and confidentiality. It gathers diverse data from several local models and aggregates it in a global model where only results are shared instead of data. This collaborative model training method aims for optimal performance. This study builds a framework for diabetes prediction using local models such as Artificial Neural Networks (ANN), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) networks, trained independently on data distributed across multiple hospitals to ensure privacy and security. Techniques like Exploratory Data Analysis (EDA) and Synthetic Minority Over-sampling Technique (SMOTE) are used to improve datasets and address class imbalance. Among the models, ANN achieves the highest accuracy (89.99%), making it the preferred choice for predictions. This approach enhances the effectiveness and reliability of diabetes prediction systems in collaborative healthcare environments. Key Words: federated learning, diabetes prediction, privacy, artificial neural networks, exploratory data analysis, SMOTE