SBTB 2023:Isaac Yang&Yuan-Ting Hsieh, NVIDIA FLARE: Federated Learning from simulation to production

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Federated Learning represents a paradigm shift from the centralized data lake focused machine learning. Instead of centralizing data in one location, federated learning allows models to be trained directly on the devices or servers where the data resides, such as smartphones, edge devices and machines. This not only preserves data privacy and security but also significantly reduces data transfer requirements, making it ideal for scenarios with sensitive data, like healthcare or finance. Federated Learning enables collaborative model training across a vast network of distributed machines or devices, leading to more personalized and efficient AI solutions while respecting user privacy. NVIDIA FLARE (NVFLARE), an open-source initiative by NVIDIA, is dedicated to bringing privacy-preserving compute and machine learning to the federated setting while maintaining simplicity and production readiness. In this presentation, we will demonstrate how NVFLARE seamlessly transforms deep learning training code into federated learning code. We will cover few examples and tutorials of large number of examples in NVFLARE repository. Federated Statistics Federated XGBoost, Linear and logistics regression, KMeans, SVM, Random Forest LLM Prompt Tuning, Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) with Nemo Training Protein Classifiers with Graph Neural Networks (GNN) Vertical Federated Learning (Vertical XGBoost and Split Learning) Enabling Cyclic and Swarm Learning Workflows Experiment Tracking with MLfl…