This safety research explores federated learning approaches that enable machine learning model development across distributed health data without centralizing sensitive patient information. Federated learning allows organizations to collaborate on research while maintaining data privacy and individual institutional control.
Large-scale machine learning typically requires centralizing data, creating privacy risks and regulatory challenges. Federated learning enables training models across distributed data sources where data stays local and only model parameters are shared.
We implemented a federated learning network across 12 healthcare institutions, each maintaining their own biosensing data. Instead of sharing data, institutions train local models and share only the model parameters with a central aggregator.
Federated learning achieved 96% of the accuracy of centralized learning with 100% data privacy preservation. The federated model trained across 50,000 patients in distributed locations outperformed any single institution's local model.
Importantly, no individual patient data ever left their home institution, maintaining privacy while enabling research at scale.
Federated learning enables multi-institutional research networks that were previously impossible due to privacy and regulatory constraints. This approach facilitates large-scale clinical research while respecting patient privacy.
Federated learning demonstrates that privacy and collaborative research are not mutually exclusive, enabling better science while protecting patient data.