Easy, file-based, offline capable federated learning
syft-flwr is an open-source framework that combines Flower's 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.
- File-Based Communication: Train models without direct network connections—communication happens via file sync (Google Drive or SyftBox)
- 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
The easiest way to get started is with our Google Colab tutorial—no local setup required:
📓 Zero-Setup FL with Google Colab
| Example | Description | Communication |
|---|---|---|
| FL Diabetes (Google Drive) | Train a diabetes prediction model across distributed Colab notebooks | Google Drive |
| FL Diabetes (SyftBox) | Train a diabetes prediction model across distributed machines | SyftBox |
| FL Diabetes (Local) | Local simulation for development and testing | Local |
| Federated Analytics | Query statistics from private datasets and aggregate them | SyftBox |
| FedRAG | Privacy-preserving question answering with RAG | SyftBox |
Install from PyPI:
pip install syft-flwrOr install from source:
pip install "git+https://github.com/OpenMined/syft-flwr.git@main"See DEVELOPMENT.md for development setup and guidelines.
See RELEASE.md for the complete release process.
