How Federated Learning Works

Federated learning lets researchers improve AI models using data from multiple institutions without sharing sensitive information. First, a researcher sends their AI model to a central system. This system then sends the model to different cancer centers to analyze their local de-identified data. The result of that analysis (but not the underlying data itself) are combined and sent back to the central system. This process repeats multiple times to refine the model, and finally, the improved model is returned to the original researcher - all without accessing identifiable patient data, and without any raw patient data ever leaving its home institution.

A complex overview of the federated learning process
Read our explainer on Federated Learning

Further Reading & Resources

To learn more about federated learning and its application in healthcare, explore these resources:

Federated Learning: Frequently Asked Questions

  • Federated learning is an AI training approach and machine learning method that preserves the anonymity of individual data. It allows researchers and clinicians to train powerful AI models that learn from participating cancer centers' millions of clinical data points while maintaining data security, privacy, and adherence to regulatory and ethical standards.

  • Instead of bringing sensitive patient data to a centralized location, federated learning brings the AI model to the data. The AI models travel to each participating cancer center’s secure data to learn from it locally. Patient data remains safely behind institutional firewalls, and individual clinical data never leaves the institution.

  • Each participating cancer center acts as an edge node, connecting to a central orchestration component. The orchestration layer sends the AI model and instructions to each edge node. Each edge node trains the AI model within its own secure environment using its local, secure data. The edge node then sends a summary of its learnings (the updated model), which contains no private patient information, back to the central orchestration layer to be aggregated and strengthen the model.

  • Federated learning enables a strategic shift leveraging collective strength rather than isolation, accelerating the pace of breakthrough discoveries by up to tenfold. The resulting AI models are more powerful and equitable because they learn from a diverse and representative sample of patients across the country. It also accelerates research for rare cancers by combining insights from small patient populations across multiple centers to uncover new patterns and potential therapies.

  • While federated learning has been gaining steam for nearly 10 years, adapting the technology for multi-institution use in cancer research has proved elusive due to significant technological, regulatory, patient privacy, and data harmonization challenges, as well as the coordination effort necessary to bring together organizations of this scale and complexity.

  • No. The central orchestration layer acts like a conductor for an orchestra; it sends the AI model and instructions to each of the edge nodes (at the cancer centers) but never sees the patient data.