Quick Start Series

Welcome to the NVIDIA FLARE Quick Start Series! This guide provides a set of hello-world examples to help you quickly learn how to build federated learning programs using NVIDIA FLARE.

Make sure you have completed the Installation steps before proceeding.

Prerequisites

• Python 3.9+

• pip

• Git

• NVFlare installed (see Installation)

Hello-world Examples

The following hello-world examples demonstrate different federated learning algorithms and workflows. Each example includes instructions and code to help you get started.

1. Hello PyTorch - Federated averaging with PyTorch models and training loops.

2. Hello Lightning - Example using PyTorch Lightning for streamlined model training.

3. Hello TensorFlow - Federated averaging using TensorFlow models.

4. Hello Logistic Regression - Federated logistic regression example using scikit-learn.

5. Hello Cyclic - Cyclic federated learning workflow example.

6. Hello Tabular Statistics - Federated statistics computation example.

7. Hello Flower - Running Flower apps in FLARE.

8. Hello XGBoost - Federated XGBoost example demonstrating gradient boosting for tabular data in a federated setting.

Let’s start with Hello PyTorch: Federated averaging with PyTorch models and training loops.