Privacy-Preserving and Generalizable Federated Learning Framework for Intelligent Healthcare Systems

A privacy-preserving and generalisable federated learning framework for intelligent healthcare systems.

A privacy-preserving and generalisable federated learning framework for intelligent 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 to work with neural networks end to end: preparing datasets, designing and training a model, tuning it, evaluating it properly and explaining the outcome.

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, dataset preparation and augmentation, model design and training, and evaluation with standard metrics, plus error analysis. The reference build uses Flower, Python, PyTorch, TensorFlow, and NumPy, with a clear project structure, setup instructions and sample data so that it can be run and extended.