Today's guest is Daniel Feller, AI Program Lead at Rhino Health. Founded in 2021, Rhino Health are activating the World’s Health Data with Federated Computing, and streamlining end-to-end healthcare research and AI development. They transform healthcare AI by integrating Edge Computing and Federated Learning into a cohesive Federated Computing strategy. This innovative approach provides AI developers with swift and secure access to healthcare data, dramatically reducing setup times from months to days and ensuring data privacy across global networks.

Federated Computing allows working with data across multiple sites while keeping that data at rest behind each site’s firewall. This technique enables multiple entities to contribute to AI model training without disclosing raw data, safeguarding the privacy of each dataset. Rhino's Federated Computing strategy ensures data integrity and compliance while minimizing latency and maximizing efficiency, making it an essential tool for advancing global healthcare solutions.

In this episode, Daniel talks about:

His background and journey to Rhino Health,

How Rhino Health uses federated learning to preserve data privacy,

Use cases of supporting research for breast cancer and CT scans,

Deploying models with Docker to ensure data control and collaboration,

How Federated Computing aids fraud detection & drug discovery by ensuring data control,

Prioritizing engineers for an agile, market-responsive federated learning infrastructure,

How Rhino enhances federated computing for data privacy & governance