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Website |
Blog |
Docs |
Conference |
Slack
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 Univerity of
Oxford, so it was build 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, MXNet, scikit-learn, TFLite, 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.dev!
A number of examples show different usage scenarios of Flower (in combination
with popular machine learning frameworks such as PyTorch or TensorFlow). To run
an example, first install the necessary extras:
Quickstart examples:
Other examples:
Experimental - curious minds can take a peek at baselines.
Flower is built by a wonderful community of researchers and engineers. Join Slack to meet them, contributions are welcome.
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 Parcollet, Titouan 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.
We welcome contributions. Please see CONTRIBUTING.md to get
started!
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Python Shell Markdown Protocol Buffer Dockerfile
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》