Package Health Score

90 / 100

security
No known security issues
popularity
Recognized
maintenance
Healthy
community
Active

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Popularity

Recognized

GitHub Stars: 5.04K
Forks: 866
Contributors: 120

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package flwr, we found that it has been starred 5,045 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Oct 14, 2024
Direct Vulnerabilities: 0
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0
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0
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0
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Indirect Vulnerabilities: 0
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0
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License Risk: 0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

Apache-2.0

Security Policy

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Maintenance

Healthy

Commit Frequency

Open Issues: 298
Open PR: 239
Last Release: 27 days ago
Last Commit: 12 days ago

Further analysis of the maintenance status of flwr based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Healthy.

We found that flwr demonstrates a positive version release cadence with at least one new version released in the past 3 months.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 120
Funding: No

A good and healthy external contribution signal for flwr project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: <4.0,>=3.9

Age: 4 years
Latest Release: 27 days ago
Dependencies: 0 Direct
Versions: 36
Maintainers: 2
Wheels: OS Independent

Readme

Flower: A Friendly Federated Learning Framework

Website | Blog | Docs | Conference | Slack

Build

Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles:

Customizable: Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case.
Extendable: Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems.
Framework-agnostic: Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand.
Understandable: Flower is written with maintainability in mind. The community is encouraged to both read and contribute to the codebase.

Meet the Flower community on flower.ai!

Federated Learning Tutorial

Flower's goal is to make federated learning accessible to everyone. This series of tutorials introduces the fundamentals of federated learning and how to implement them in Flower.

What is Federated Learning?

(or open the Jupyter Notebook)
An Introduction to Federated Learning

(or open the Jupyter Notebook)
Using Strategies in Federated Learning

(or open the Jupyter Notebook)
Building Strategies for Federated Learning

(or open the Jupyter Notebook)
Custom Clients for Federated Learning

(or open the Jupyter Notebook)

Stay tuned, more tutorials are coming soon. Topics include Privacy and Security in Federated Learning, and Scaling Federated Learning.

30-Minute Federated Learning Tutorial

(or open the Jupyter Notebook)

Documentation

Flower Docs:

Flower Baselines

Flower Baselines is a collection of community-contributed projects that reproduce the experiments performed in popular federated learning publications. Researchers can build on Flower Baselines to quickly evaluate new ideas. The Flower community loves contributions! Make your work more visible and enable others to build on it by contributing it as a baseline!

Please refer to the Flower Baselines Documentation for a detailed categorization of baselines and for additional info including:

Flower Usage Examples

Several code examples show different usage scenarios of Flower (in combination with popular machine learning frameworks such as PyTorch or TensorFlow).

Quickstart examples:

Other examples:

Community

Flower is built by a wonderful community of researchers and engineers. Join Slack to meet them, contributions are welcome.

Citation

If you publish work that uses Flower, please cite Flower as follows:

@article{beutel2020flower,
  title={Flower: A Friendly Federated Learning Research Framework},
  author={Beutel, Daniel J and Topal, Taner and Mathur, Akhil and Qiu, Xinchi and Fernandez-Marques, Javier and Gao, Yan and Sani, Lorenzo and Kwing, Hei Li and Parcollet, Titouan and Gusmão, Pedro PB de and Lane, Nicholas D},
  journal={arXiv preprint arXiv:2007.14390},
  year={2020}
}

Please also consider adding your publication to the list of Flower-based publications in the docs, just open a Pull Request.

Contributing to Flower

We welcome contributions. Please see CONTRIBUTING.md to get started!

FAQs

What is flwr?

Flower: A Friendly Federated Learning Framework. Visit Snyk Advisor to see a full health score report for flwr, including popularity, security, maintenance & community analysis.

Is flwr popular?

The python package flwr receives a total of 6,800 weekly downloads. As such, flwr popularity was classified as a recognized. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is flwr well maintained?

We found that flwr demonstrated a healthy version release cadence and project activity. It has a community of 120 open source contributors collaborating on the project. See the full package health analysis to learn more about the package maintenance status.

Is flwr safe to use?

The python package flwr was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 9 November-2024, at 08:56 (UTC).

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