Federated Learning Framework
Developed a privacy-preserving federated learning framework for healthcare applications. Enables collaborative model training without sharing sensitive patient data.
Technologies Used
Project Overview
Created a comprehensive federated learning framework specifically designed for healthcare applications where data privacy is paramount. The system enables multiple healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data.
Core Components: • Secure aggregation protocols • Differential privacy mechanisms • Byzantine fault tolerance • Adaptive client selection • Comprehensive audit logging
The framework supports various healthcare use cases including diagnostic imaging, electronic health records analysis, and drug discovery. It has been tested with 10+ healthcare partners across different geographical regions.
Key Challenges
- Ensuring strong privacy guarantees
- Handling heterogeneous data distributions
- Managing communication overhead
Impact & Results
Enables collaborative healthcare AI research while maintaining strict privacy requirements. Currently being evaluated by FDA for regulatory approval.