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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Flower: A Friendly Federated AI Framework
| Date | Stars |
|---|---|
| 2026-07-24 | 7051 |
| 2026-07-25 | 7052 |
| 2026-07-28 | 7052 |
| 2026-07-30 | 7052 |
| 2026-08-06 | 7052 |
Today
— stars today
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— stars this week
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Momentum
15.0
growth rate 0.00%/day
# Flower: A Friendly Federated AI Framework
<p align="center">
<a href="https://flower.ai/">
<img src="https://flower.ai/static/images/icon/icon.png" width="140px" alt="Flower Website" />
</a>
</p>
<p align="center">
<a href="https://flower.ai/">Website</a> |
<a href="https://flower.ai/blog">Blog</a> |
<a href="https://flower.ai/docs/">Docs</a> |
<a href="https://flower.ai/join-slack">Slack</a>
<br /><br />
</p>
[](https://github.com/flwrlabs/flower/blob/main/LICENSE)
[](https://github.com/flwrlabs/flower/blob/main/CONTRIBUTING.md)
[](https://pepy.tech/project/flwr)
[](https://hub.docker.com/u/flwr)
[](https://flower.ai/join-slack)
Flower (`flwr`) is a framework for building federated AI 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](https://pytorch.org), [TensorFlow](https://tensorflow.org), [Hugging Face Transformers](https://huggingface.co/), [PyTorch Lightning](https://pytorchlightning.ai/), [scikit-learn](https://scikit-learn.org/), [JAX](https://jax.readthedocs.io/), [TFLite](https://tensorflow.org/lite/), [MONAI](https://docs.monai.io/en/latest/index.html), [fastai](https://www.fast.ai/), [MLX](https://ml-explore.github.io/mlx/build/html/index.html), [XGBoost](https://xgboost.readthedocs.io/en/stable/), [CatBoost](https://catboost.ai/), [LeRobot](https://github.com/huggingface/lerobot) for federated robots, [Pandas](https://pandas.pydata.org/) for federated analytics, or even raw [NumPy](https://numpy.org/)
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](https://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.
0. **[What is Federated Learning?](https://flower.ai/docs/framework/main/en/tutorial-series-what-is-federated-learning.html)**
1. **[Get started with Flower](https://flower.ai/docs/framework/main/en/tutorial-series-get-started-with-flower.html)**
2. **[Write your first Flower App](https://flower.ai/docs/framework/main/en/tutorial-series-write-your-first-flower-app.html)**
3. **[Write your first Flower App with PyTorch](https://flower.ai/docs/framework/main/en/tutorial-series-write-your-first-flower-app-pytorch.html)**
4. **[Use a federated learning strategy](https://flower.ai/docs/framework/main/en/tutorial-series-use-a-federated-learning-strategy-pytorch.html)**
5. **[Customize a Flower Strategy](https://flower.ai/docs/framework/main/en/tutorial-series-build-a-strategy-from-scratch-pytorch.html)**
6. **[Communicate Custom Messages](https://flower.ai/docs/framework/main/en/tutorial-series-customize-the-client-pytorch.html)**
Stay tuned, more tutorials are coming soon. Topics include **Privacy and Security in FederaExcerpt of 11,685 characters
Read on GitHub
Javier
818
810
Heng Pan · Flower Labs @flwrlabs · United Kingdom
800
660
Chong Shen Ng
401
Taner Topal · Flower Labs GmbH · Germany
349
Adam Narozniak · Poland
234
148
117
116
106
61
Flower · Flower Labs
42
39
37
Maria Boerner · Germany
28
William Lindskog · Flower Labs · United States
25
25
20
Can Türk
19
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:17c0134702062aec, topic:deep-learning, topic:pytorch, topic:tensorflow