ThesisResearchAI/ML 6-12 months
Federated Learning for Healthcare Privacy-Preserving Diagnostics
A federated learning framework where multiple hospitals train a shared diagnostic AI model without sharing patient data — preserving privacy while achieving collective intelligence for disease detection.
PythonPySyft / FlowerPyTorchFastAPIDockerDifferential Privacy
Find GitHub repos & source code →How to build it — step by step
- 1Literature Review: Survey federated learning papers: McMahan et al. FedAvg, FedProx, FedNova — understand aggregation strategies
- 2Simulation Setup: Simulate 5-10 hospital nodes with IID and non-IID data distributions using Chest X-ray dataset
- 3Privacy Guarantees: Implement differential privacy (ε-DP) with Gaussian noise addition before gradient sharing
- 4Evaluation: Compare federated model accuracy vs centralized baseline; measure privacy budget vs accuracy tradeoff
Key features to implement
- ✓Federated training across simulated hospital nodes
- ✓Differential privacy with configurable ε budget
- ✓Secure aggregation to prevent gradient inversion
- ✓Performance comparison: federated vs centralized
- ✓Privacy budget vs accuracy Pareto curve analysis
💡 Unique twist to stand out
Investigate personalized federated learning (pFedMe / Ditto) where each hospital node fine-tunes the global model on local data — improving accuracy for hospitals with unique patient populations.
🎓 What you'll learn
Federated learning algorithms, differential privacy, distributed machine learning, healthcare AI ethics, and research paper writing methodology.