Framework Agnostic

NVIDIA FLARE is compatible with any machine learning or deep learning framework. This versatility is demonstrated through various example repositories, including those for PyTorch, TensorFlow, PyTorch Lightning, XGBoost, Scikit-learn, GraphSAGE, Hugging Face, NeMo, Bio-Nemo, and MONAI. Most machine learning problems can easily be converted from centralized algorithms to federated algorithms using NVIDIA FLARE.

Domain Agnostic

NVIDIA FLARE is also domain agnostic, making it suitable for applications in medical imaging, drug discovery, self-driving cars, financial services, medical devices, energy, and more. This broad applicability is reflected in the diverse industries of our customers.

Model Agnostic

NVIDIA FLARE supports a wide variety of models, including decision trees, classification, regression, various types of LLM fine-tuning, and XGBoost. Regardless of the model type, NVIDIA FLARE can work with them all.

Task Agnostic

Thanks to its generic design, NVIDIA FLARE is task agnostic. It can handle any type of task or payload, without mandating a specific type of computation. NVIDIA FLARE provides coordination and communication, allowing users to leverage its capabilities for tasks such as Federated Statistics, Multi-Party Private Set Intersection (PSI), and Federated Retrieval-Augmented Generation (RAG) tasks.