With traditional centralized AI, all the data is combined into a single data set for training the AI model. In contrast, with federated learning, the data never leaves its original location.
Federated learning gives these industries a way to learn from multiple organizations' data while still keeping tight controls and limiting exposure.
In healthcare, hospitals collaboratively train models for cancer diagnosis, brain tumor segmentation and COVID-19 detection without sharing patient records.
For example, U.S. medical centers — including collaborators from Case Western Reserve University; Georgetown University; Mayo Clinic; the University of California, San Diego; the University of Florida; and Vanderbilt University — are using NVIDIA-powered federated learning for tumor segmentation, according to an NVIDIA blog post.
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