Privacy-Preserving Diabetes Prediction Using Federated Learning in Distributed Healthcare Systems

A privacy-preserving diabetes prediction system that uses federated learning across distributed healthcare systems.

A privacy-preserving diabetes prediction system that uses federated learning across distributed healthcare systems. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want hands-on experience of the whole machine learning workflow: data collection and cleaning, feature engineering, model training and tuning, evaluation and presenting results.

The main parts are privacy-preserving training across several clients, with only model updates shared, security and privacy controls such as encryption and access control, data preparation and exploration, model training and comparison of algorithms, and evaluation with standard metrics. The reference build uses Flower, Python, scikit-learn, Pandas, and Flask, with a clear project structure, setup instructions and sample data so that it can be run and extended.