Flower Framework Documentation¶
Welcome to Flower’s documentation. Flower is a friendly federated learning framework.
Join the Flower Community¶
The Flower Community is growing quickly - we’re a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
Flower Framework¶
The user guide is targeted at researchers and developers who want to use Flower to bring existing machine learning workloads into a federated setting. One of Flower’s design goals was to make this simple. Read on to learn more.
Tutorials¶
A learning-oriented series of federated learning tutorials, the best place to start.
Tutorial
QUICKSTART TUTORIALS: PyTorch | TensorFlow | MLX | 🤗 Transformers | JAX | Pandas | fastai | PyTorch Lightning | scikit-learn | XGBoost | Android | iOS
We also made video tutorials for PyTorch:
And TensorFlow:
How-to guides¶
Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
How-to guides
- Install Flower
- Configure Clients
- Design stateful ClientApps
- Use strategies
- Implement strategies
- Aggregate evaluation results
- Save and Load Model Checkpoints
- Run simulations
- Enable TLS connections
- Use Built-in Mods
- Use Differential Privacy
- Authenticate SuperNodes
- Implement FedBN
- Run Flower on Azure
- Use CLI JSON output
- Run Flower using Docker
- Upgrade to Flower 1.0
- Upgrade to Flower 1.13
Explanations¶
Understanding-oriented concept guides explain and discuss key topics and underlying ideas behind Flower and collaborative AI.
Explanations
References¶
Information-oriented API reference and other reference material.
Flower main package. |
Reference docs
Contributor docs¶
The Flower community welcomes contributions. The following docs are intended to help along the way.
Contributor tutorials
Contributor how-to guides
Contributor explanations
Contributor references