Federated Cancer Prediction

Enabled privacy-preserving cancer risk prediction using federated machine learning.

Key Takeaways:

Data privacy limited collaborative modeling

Sensitive patient data prevented institutions from training shared predictive models.

We enabled privacy-preserving collaboration

Federated learning allowed joint model training without sharing raw patient data.

Federated ML with NVIDIA FLARE

Models were trained using PyTorch across structured, time-series, and unstructured clinical data within a federated setup.

Promising early cancer risk prediction performance

Initial results demonstrated strong predictive potential while preserving patient privacy.

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Governed Metrics for AI

Single logic across every interface

Compliance Reporting

Reporting automated

Audience Segmentation

Targeting efficiency improved.