https://github.com/openmined/syft-flwr
Federated Learning Made Easy
https://github.com/openmined/syft-flwr
Last synced: 3 months ago
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Federated Learning Made Easy
- Host: GitHub
- URL: https://github.com/openmined/syft-flwr
- Owner: OpenMined
- License: apache-2.0
- Created: 2025-03-11T09:00:29.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-23T12:01:14.000Z (12 months ago)
- Last Synced: 2025-06-25T06:47:35.892Z (12 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 15.1 MB
- Stars: 1
- Watchers: 5
- Forks: 0
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# syft-flwr
**Easy, file-based, offline capable federated learning**
`syft-flwr` is an open-source framework that combines [Flower's](https://github.com/adap/flower) federated learning capabilities with file-based communication. Train machine learning models collaboratively across distributed datasets without centralizing data—with easy setup, offline capability, and no servers required.
## Key Features
- **File-Based Communication**: Train models without direct network connections—communication happens via file sync (Google Drive or [SyftBox](https://syftbox.net/))
- **Zero Infrastructure**: No servers to maintain, no complex networking setup—just notebooks and file sync
- **Offline Capable**: Asynchronous message passing enables training even with intermittent connectivity
- **Privacy by Design**: Data never leaves its source—only model updates are shared
- **Flower Integration**: Built on Flower's robust FL framework—supports FedAvg, custom strategies, and all standard Flower features
## Quick Start
The easiest way to get started is with our **Google Colab tutorial**—no local setup required:
📓 [Zero-Setup FL with Google Colab](notebooks/fl-diabetes-prediction/distributed-gdrive/README.md)
## Example Notebooks
| Example | Description | Communication |
|---------|-------------|---------------|
| [FL Diabetes (Google Drive)](notebooks/fl-diabetes-prediction/distributed-gdrive/README.md) | Train a diabetes prediction model across distributed Colab notebooks | Google Drive |
| [FL Diabetes (SyftBox)](notebooks/fl-diabetes-prediction/distributed/README.md) | Train a diabetes prediction model across distributed machines | SyftBox |
| [FL Diabetes (Local)](notebooks/fl-diabetes-prediction/local/README.md) | Local simulation for development and testing | Local |
| [Federated Analytics](notebooks/federated-analytics-diabetes/README.md) | Query statistics from private datasets and aggregate them | SyftBox |
| [FedRAG](notebooks/fedrag/README.md) | Privacy-preserving question answering with RAG | SyftBox |
## Installation
Install from [PyPI](https://pypi.org/project/syft-flwr/):
```bash
pip install syft-flwr
```
Or install from source:
```bash
pip install "git+https://github.com/OpenMined/syft-flwr.git@main"
```
## Development
See [DEVELOPMENT.md](DEVELOPMENT.md) for development setup and guidelines.
### Releasing
See [RELEASE.md](RELEASE.md) for the complete release process.
## Community
- 💬 [Slack](https://slack.openmined.org/) - Join #support-syftbox for questions
- 🐛 [Issues](https://github.com/OpenMined/syft-flwr/issues) - Report bugs or request features
- 🌟 Star this repo to support the project!
## License
[Apache 2.0](LICENSE)