Introduction to Federated Learning with NVIDIA FLARE
This self-paced course offers a comprehensive introduction to federated learning using NVIDIA FLARE (Federated Learning Application Runtime Environment). Participants will learn to develop and deploy federated learning applications, transitioning from traditional machine learning workflows to decentralized, privacy-preserving models. The course includes practical examples and guidance suitable for both beginners and experienced practitioners.
Who Should Take This Course:
Data scientists, AI engineers, and researchers interested in implementing federated learning solutions. A foundational understanding of machine learning concepts is recommended.
What You Will Learn:
• Fundamentals of federated learning and its advantages
• How to use NVIDIA FLARE to train and deploy federated learning models
• Transitioning from standard ML code to federated learning workflows
• Customizing client and server logic in NVIDIA FLARE
• Understanding job structure, configuration, and statistics
Why This Course Matters:
Federated learning enables collaborative AI model development without centralizing data, ensuring privacy and security. NVIDIA FLARE provides a robust framework for implementing federated learning in real-world applications across various industries.