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 (10 months ago)
• Default Branch: main
• Last Pushed: 2025-06-23T12:01:14.000Z (7 months ago)
• Last Synced: 2025-06-25T06:47:35.892Z (7 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

`syft_flwr` is an open source framework that facilitate federated learning (FL) projects using [Flower](https://github.com/adap/flower) over the [SyftBox](https://github.com/OpenMined/syftbox) protocol

![FL Training Process](https://github.com/OpenMined/syft-flwr/raw/main/notebooks/fl-diabetes-prediction/images/fltraining.gif)

## Example Usages

Please look at the `notebooks/` folder for example use cases:

-  [FL diabetes prediction](notebooks/fl-diabetes-prediction/README.md) shows how to train a federated model over distributed machines for multiple rounds

-  [Federated analytics](notebooks/federated-analytics-diabetes/README.md) shows how to query statistics from private datasets from distributed machines and then aggregate them

-  [FedRAG (Federated RAG)](notebooks/fedrag/README.md) demonstrates privacy-preserving question answering using Retrieval Augmented Generation across distributed document sources with remote data science workflow

## Development

### Releasing

See [RELEASE.md](RELEASE.md) for the complete release process.