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.
User Guide#
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.
User Guide
- Installing Flower
- Quickstart PyTorch
- Quickstart TensorFlow
- Quickstart 🤗 Transformers
- Quickstart PyTorch Lightning
- Quickstart MXNet
- Quickstart scikit-learn
- Evaluation
- Configuring Clients
- Strategies
- Implementing Strategies
- Guide: Saving Progress
- Guide: SSL-enabled Server and Client
- Usage Examples
- 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
- Changelog
- FAQ
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.
Baselines
API Reference#
API Reference
Contributor Guide#
The Flower authors welcome external contributions. The following guides are intended to help along the way.
About#
About