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 | 🤗 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
- Use strategies
- Implement strategies
- Aggregate evaluation results
- Save and load model checkpoints
- Run simulations
- Monitor simulation
- Configure logging
- Enable SSL connections
- Use Built-in Mods
- Use Differential Privacy
- Authenticate SuperNodes
- Run Flower using Docker
- Upgrade to Flower 1.0
- Upgrade to Flower Next
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