Flower 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 | PyTorch Lightning | MXNet | scikit-learn
How-to guides#
Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
How-to guides
- Installing Flower
- Configure Clients
- Strategies
- Implementing Strategies
- Save Progress
- Saving and Loading PyTorch Checkpoints
- SSL-enabled Server and Client
- Example: Walk-Through PyTorch & MNIST
- Example: PyTorch - From Centralized To Federated
- Example: MXNet - Run MXNet Federated
- Example: JAX - Run JAX Federated
- Example: FedBN in PyTorch - From Centralized To Federated
- Virtual Env Installation
- Upgrade to Flower 1.0
Explanations#
Understanding-oriented concept guides explain and discuss key topics and underlying ideas behind Flower and collaborative AI.
Explanations
Reference#
Information-oriented API reference and other reference material.
Reference docs
Flower Baselines#
Flower Baselines are a collection of organised scripts used to reproduce results from well-known publications or benchmarks. You can check which baselines already exist and/or contribute your own baseline.
Flower Baselines
Contributor Guide#
The Flower authors welcome external contributions. The following guides are intended to help along the way.