Tutorial: Train Skin Cancer AI on Hospital Data Without Accessing Raw Images

A developer guide published on DEV Community explains how to build a privacy-preserving skin cancer classifier using Federated Learning, PySyft, and PyTorch. The approach addresses a core challenge in medical AI: hospitals cannot share patient data due to regulations like HIPAA and GDPR. Federated Learning solves this by sending the model to the data rather than centralizing the data itself, meaning only encrypted model gradients — not raw images — leave each hospital. The tutorial simulates two hospital nodes and incorporates Differential Privacy via Opacus to guard against membership inference attacks. The method is demonstrated using the HAM10000 skin lesion dataset as a reference use case.