Data is the new oil, but in healthcare, data is more like plutonium—extremely valuable but incredibly dangerous if handled incorrectly. If you are building AI for medical use cases, you've likely hit the "Data Silo" wall. Hospitals can't just ZIP up patient records and DM them to you because of GDPR, HIPAA, and basic human ethics.
So, how do we train a high-performing Skin Lesion Classification model without ever actually seeing the raw medical images? Welcome to the world of Federated Learning (FL) and Privacy-Preserving AI. In this guide, we’ll explore how to use PySyft and PyTorch to train models on decentralized data while keeping sensitive information exactly where it belongs: with the patient.
We will focus on Federated Learning, Differential Privacy, and Secure Multi-Party Computation (SMPC) to build a robust, privacy-first pipeline.
The Architecture: Move the Code, Not the Data
In traditional Machine Learning, we bring data to the model. In Federated Learning, we flip the script: we bring the model to the data.
