Welcome to Flower’s documentation. Flower is a friendly federated learning framework.
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The Flower Community is growing quickly - we’re a friendly group of researchers, engineers, students, professionals, academics, and other enthusiasts.
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.
A learning-oriented series of federated learning tutorials, the best place to start.
QUICKSTART TUTORIALS: PyTorch | TensorFlow | 🤗 Transformers | JAX | Pandas | fastai | PyTorch Lightning | MXNet | scikit-learn | XGBoost
Problem-oriented how-to guides show step-by-step how to achieve a specific goal.
- Installing Flower
- Configure Clients
- Implementing Strategies
- Save Progress
- Saving and Loading PyTorch Checkpoints
- Monitor Simulation
- 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
Understanding-oriented concept guides explain and discuss key topics and underlying ideas behind Flower and collaborative AI.
Information-oriented API reference and other reference material.
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.
The Flower authors welcome external contributions. The following guides are intended to help along the way.