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Federated Learning Integration

Unlock collaborative intelligence — without sharing patient data.

  • The federated learning module enables multiple institutions to collaboratively train AI models on decentralized data. Each partner retains data locally while contributing to a shared global model.

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  • This approach empowers large-scale model development without breaching data privacy laws or ethics constraints.

Capabilities

Cross-institutional model aggregation with differential privacy
Compatibility with major frameworks (TensorFlow Federated, PySyft, Flower)
Secure orchestration and audit logs for compliance
API connectors for hospitals, labs, and research networks

Request Integration Access

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