Federated Learning from Simulation to Production with NVIDIA FLARE
NVIDIA FLARE 2.2 includes a host of new features that reduce development time and accelerate deployment for federated learning, helping organizations cut costs...
Using Federated Learning to Bridge Data Silos in Financial Services
Unlocking the full potential of artificial intelligence (AI) in financial services is often hindered by the inability to ensure data privacy during machine...
Experimenting with Novel Distributed Applications Using NVIDIA Flare 2.1
NVIDIA FLARE (NVIDIA Federated Learning Application Runtime Environment) is an open-source Python SDK for collaborative computation. FLARE is designed with a...
In NVIDIA Clara Train 4.0, we added homomorphic encryption (HE) tools for federated learning (FL). HE enables you to compute data while the data is still...
Clara Train 3.1 Brings Secure, Enterprise-Grade Federated Learning to Developers
NVIDIA recently released Clara Train 3.1 for healthcare developers to collaborate on secure, enterprise-grade AI models. Building robust AI can be a challenge...
Transforming AI Healthcare with Federated Learning
NVIDIA researchers, in collaboration with Owkin scientists, a premier member of NVIDIA Inception, as well as other scientists, this week published a new...
AI requires massive amounts of data. This is particularly true for industries such as healthcare. For example, training an automatic tumor diagnostic system...
From Federated Learning to Embedded AI: NVIDIA Clara Brings AI to the Edge for Developers
At RSNA 2019, the annual meeting of the Radiological Society of North America, NVIDIA announced updates to the Clara Application Framework that takes...
