FATE supports federated learning through an industrial grade framework.
Open source
24 alternativesFlower
Flower provides a federated AI framework for machine learning projects.
About Flower
Flower is an open source framework for building federated AI systems, which train machine learning models across many separate data sources without centralizing the data. The project's GitHub repository serves as its homepage. The README describes Flower as customizable, extendable, framework-agnostic, and written to be readable and maintainable. The project originated from a research project at the University of Oxford and was built with AI research in mind.
The repository includes a federated learning tutorial series, documentation for installation and quickstarts, a collection of community-contributed baselines that reproduce experiments from federated learning publications, and code examples for several use cases. Pricing is not stated in the source.
Key features
- Works with PyTorch, TensorFlow, scikit-learn, JAX, XGBoost, and other machine learning frameworks
- Configurable for different federated learning use cases
- Components can be extended and overridden to build new system designs
- Community-contributed baselines that reproduce published federated learning experiments
- Quickstart examples for Android (TFLite) and iOS (CoreML) targets
- Tutorial series covering federated learning fundamentals and custom strategies
Best for: Machine learning researchers and developers who build federated learning systems and want to use an existing machine learning framework.
Good for
- Compatible with many machine learning frameworks, including PyTorch, TensorFlow, and scikit-learn
- Open source, with a public GitHub repository
- Includes tutorials, quickstart guides, and usage examples
Watch out for
- Several planned tutorial topics, including privacy and security and scaling, are listed as coming soon
Flower at a glance
- Website
- github.com/adap/flower
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Flower FAQ
Which machine learning frameworks does Flower support?
The README says Flower can be used with any machine learning framework and names examples including PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, CatBoost, and raw NumPy.
Where can I find learning materials for Flower?
The repository links to a federated learning tutorial series, which introduces federated learning fundamentals and how to implement them in Flower. The source states that more tutorials are coming, including topics on privacy, security, and scaling.
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