How Federated Learning can Bring Positive Changes for Healthcare CIOs

While federated learning trains an algorithm across many decentralized edge devices rather than running an analysis on data uploaded to a single server, researchers believe it will be the next wave of AI

Fremont, CA: During the COVID-19 crisis, healthcare organizations were forced to deploy large-scale health tech capabilities. Because the industry deals with massive amounts of patient health data, it is the responsibility of healthcare CIOs to secure them by leveraging advanced solutions. They must seek out unrivaled techniques and tools that can assist them in extracting more value from data and driving revenue streams and innovation within their organizations. Ironically, today's CIOs have the opportunity to integrate changing technologies, one of which is federated learning. It is a machine learning framework that enables AI algorithms to learn from data that is spread across multiple locations.

Federated learning allows a network of participants to collaboratively train algorithms on data while keeping each stakeholder's data in its home location. This machine learning technique can also protect and secure data. Federated learning, in accordance with HIPPA and GDPR requirements, keeps all data housed in healthcare organizations' networks at all times.