Learn about federated learning through a comprehensive tech talk that explores NVIDIA FLARE, an open-source SDK designed for privacy-preserving collaborative AI model development. Discover how this lightweight Python package enables distributed training without centralizing data, incorporating advanced security features like encryption and differential privacy. Explore the seamless integration capabilities with PyTorch and TensorFlow, while examining real-world applications in medical imaging, biopharma, and finance. Gain insights from NVIDIA speaker Holger Roth on implementing customizable workflows and leveraging cutting-edge FL algorithms for secure, efficient cross-enterprise collaboration in privacy-sensitive environments.
NVIDIA Federated Learning - Concepts, Technology, and Use Cases
Overview
Syllabus
NVIDIA Federated Learning: Concepts, Technology, and Use Cases
Taught by
AICamp
Related Courses
-
Federated Learning and Privacy-preserving RAGs
-
Secure and Private AI
-
Federated Learning for Medical Image Analysis - Seminar 89
-
Federated Learning - Machine Learning on Decentralized Data
5.0 -
Privacy-Aware Compression for Federated Learning
-
Federated User Representation Learning: Privacy-Preserving Personalization in Distributed Systems
