Federated Learning Application Runtime Environment for Developing Robust AI Models


Mar 10, 12:00 PM PST
  • Virtual SF Big Analytics
  • 54 RSVP
Description
Speaker

This event is hosted by SF Big Analytics Group. https://www.meetup.com/SF-Big-Analytics

Federated learning (FL) enables building robust and generalizable AI models by leveraging diverse datasets from multiple collaborators without moving data. We created NVIDIA FLARE as an open-source SDK to make it easier for data scientists to use FL in their research. The SDK allows existing machine learning and deep learning workflows adapted for distributed learning across enterprises and enables platform developers to build a secure, privacy-preserving offering for multiparty collaboration utilizing homomorphic encryption or differential privacy. The SDK is a lightweight, flexible, and scalable Python package and allows researchers to bring their data science workflows implemented in any training libraries (PyTorch, TensorFlow, or even NumPy), and apply them in real-world FL settings.

This talk will introduce the key design principles of NVIDIA FLARE and illustrate use cases (e.g., COVID analysis) with customizable FL workflows that implement different privacy-preserving algorithms.

Holger Roth (Nvidia)

Sr. Applied Research Scientist at NVIDIA focusing on deep learning for medical imaging. He has been working closely with clinicians and academics over the past several years to develop deep learning based medical image computing and computer-aided detection models for radiological applications. He is an Associate Editor for IEEE Transactions of Medical Imaging and holds a Ph.D. from University College London, UK. In 2018, he was awarded the MICCAI Young Scientist Publication Impact Award
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