I need a straightforward, well-commented Python prototype that shows how federated learning can be applied to healthcare data processing—more precisely, to medical image analysis on mammograms. Scope • Build a minimal yet functional simulation where several virtual clients train a shared convolutional model on local mammogram data while keeping the raw images private. • Use an open-source framework that already supports federated workflows—TensorFlow Federated, Flower, or PySyft—whichever lets you move fastest. • Include synthetic or public mammogram samples so the code runs out-of-the-box (no licensed datasets required). Core Requirements 1. Clear project structure with a server-client loop, model aggregation, and at least three simulated clients. 2. A small CNN suitable for image classification; accuracy reporting after each aggregation round. 3. Comments explaining the key privacy-preserving steps and any assumptions. 4. A short README with setup steps, how to launch the simulation, and where to plug in real data later. Deliverables • All source code and requirements.txt/conda-environment.yml. • The README and a brief note on possible next steps (e.g., adding differential privacy or secure aggregation). I’m aiming for a concise, functional demo rather than a full production system, so focus on getting the essential pieces working cleanly and quickly.