Federated learning has emerged as a promising solution to the problem of data privacy and locality in training robust AI models. By enabling distributed model training without data sharing, federated learning allows researchers and data scientists to train generalizable models across diverse, distributed datasets while maintaining data privacy. We'll introduce the NVIDIA FLARE platform for federated learning and its communication model and deployment architecture, highlight tools for developing federated learning workflows, and outline the journey from development to production deployment. Hands-on tutorials will cover FLARE application development with the FL Simulator and distributed deployment of federated learning workflows with the FLARE Dashboard. We'll also cover MONAI FL integration, showing streamlined federated training of medical imaging models based on the MONAI Bundle model format.
Prerequisite(s):
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