Federated learning feeds more data to support AI processes

By
The Rhino Team
No items found.
December 2, 2021

Federated learning allows AI models to be trained on data from multiple healthcare organizations without the need to centralize information, thereby preserving patient privacy and data security. This approach facilitates collaboration among institutions, enabling the development of more robust and generalizable AI models. During the COVID-19 pandemic, federated learning was instrumental in predictive modeling, such as forecasting patients’ oxygen requirements, by utilizing diverse datasets from various sources. Its application holds promise for advancing research in radiology and other medical fields by overcoming data-sharing obstacles and enhancing the quality of AI-driven insights.

For a comprehensive analysis, read the full article on Health Data Management.

Build your Federated Network