Collaborative platform for federated learning with advanced privacy guarantees. Implements differential privacy and secure aggregation for distributed machine learning across organizations.
This platform enables multiple organizations to collaboratively train machine learning models without sharing sensitive data. The system provides strong privacy guarantees through a combination of cryptographic and statistical techniques.
Core components:
- Secure multi-party computation for model aggregation
- Differential privacy mechanisms for gradient perturbation
- Byzantine-robust aggregation algorithms
- Containerized deployment with Kubernetes orchestration
The platform has been successfully deployed across healthcare institutions for medical image analysis, demonstrating the feasibility of privacy-preserving collaborative ML while maintaining model performance comparable to centralized training.