Improving health care means improving data diversity. Federated learning can help.

By
The Rhino Team
No items found.
April 30, 2021

Federated learning allows AI models to be trained across multiple healthcare institutions without the need to centralize data. This approach enhances data diversity, leading to more robust and generalizable AI models. By keeping patient data within local systems, federated learning also ensures privacy and complies with data protection regulations. Implementing this method can improve diagnostic accuracy and patient outcomes by leveraging a wider range of data sources.

For a comprehensive analysis, read the full article on STAT News.

Build your Federated Network