Nvidia Corp. wants to make artificial intelligence a staple of the healthcare industry with a new distributed learning technique announced today that can train machine learning models while protecting patient privacy. AI holds great promise, but for industries such as healthcare where data privacy is of paramount importance, tapping into that potential is a big challenge. The problem is that any data that might be useful to train models is almost always confidential, which means it can't be shared with technology partners. Nvidia reckons it can solve this problem with its new Clara Federated Learning technique, which ensures that patient data remains within healthcare providers' systems at all times. Clara FL is a reference application for distributed AI training that's designed to run on Nvidia's recently announced EGX intelligent edge computing platform.

AI researchers from Nvidia and King's College London have used federated learning to train a neural network for brain tumor segmentation, a milestone Nvidia claims is a first for medical image analysis. The technique can allow data-sharing between hospitals and researchers while preserving patient privacy. Federated learning is an approach to machine learning that -- when using a client-server approach -- can eliminate the need to create a single data lake in order to train models. Instead, models are trained locally on devices that then transfer insights from multiple machines to a central model. "You need to get to these innovations, and I believe there's kind of two ways. One, which we released last August, is create the best generalizable model that you have today and just send it to each one of these hospitals, where they can localize it for their own patients," Nvidia director of healthcare Abdul Halabi told VentureBeat in a phone interview.