← Back to Services🔗Intel OpenFL — Federated Learning Without Data Centralization
federated-learning • Healthcare & Multi-Enterprise AI
Intel OpenFL is an open-source framework for federated learning that enables organizations to collaboratively train AI models without sharing raw data. Each participant trains on local data, and only model weight updates (gradients) are shared — keeping sensitive data behind organizational firewalls. Projects include medical imaging AI across hospitals without sharing patient scans.
Features
- •Horizontal and vertical federated learning supported with aggregation director
- •Differential privacy and secure aggregation prevent gradient leakage
- •Support for PyTorch and TensorFlow training pipelines
- •Medical imaging proof-of-concept: 20-institution tumor segmentation model
- •Intel SGX enclave support for hardware-secured collaborative training
- •Federated analytics: compute statistics across silos without data movement
Benefits
- •Train on 10x more data by collaborating without sharing
- •HIPAA-compliant multi-hospital AI model development
- •Banks collaboratively detect fraud without exposing customer transactions
- •5G network optimization across carriers without sharing subscriber data
- •Regulatory compliance: data never leaves organizational boundaries