A Little-Known A.I. Method Can Train on Your Health Data Without Threatening Your Privacy
Machine learning has great potential to transform disease diagnosis and detection, but it’s been held back by patients’ reluctance to give up access to sensitive information
By Karen Hao
In 2017, Google quietly published a blog post about a new approach to machine learning. Unlike the standard method, which requires the data to be centralized in one place, the new one could learn from a series of data sources distributed across multiple devices. The invention allowed Google to train its predictive text model on all the messages sent and received by Android users — without ever actually reading them or removing them from their phones.
Despite its cleverness, federated learning, as the researchers called it, gained little traction within the A.I. community at the time. Now that is poised to change as it finds application in a completely new area: its privacy-first approach could very well be the answer to the greatest obstacle facing A.I. adoption in health care today.
“There is a false dichotomy between the privacy of patient data and the utility of the data to society,” says Ramesh Raskar, an MIT…
