The NVIDIA Clara™ Train v3.1 release includes Enterprise-Grade Federated Learning.
In order to build robust AI algorithms, hospitals and medical institutions often need to collaboratively share and combine their local knowledge. However, this is challenging because patient data is private by nature. It is vital to train algorithms without compromising privacy.
Federated learning, a type of distributed AI model development technique, allows creating such models without transferring data outside of hospitals or imaging centers. Federate Learning enables model development, training, and evaluation to occur on the hospital’s edge nodes, keeping hospital data private.
NVIDIA Clara™ Train v3.1 enables Enterprise-grade, secure Federated Learning with ease of provisioning for Client IT sites, secure authentication, and flexible authorization policies for server-client communication. Additionally, we see a 10x increase in iterative research experimentation for multi-institutional collaborative model development.
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