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.
- Installing Flower
- Quickstart PyTorch
- Quickstart TensorFlow
- Quickstart 🤗 Transformers
- Quickstart PyTorch Lightning
- Quickstart MXNet
- Quickstart scikit-learn
- Configuring Clients
- 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
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.