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Decision brief
A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.
Good fit when
- When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning
- If your project benefits from an easily extendable design developed with AI research needs in mind
Avoid when
- Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support
- Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
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Install
pip install flowerSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Flower is a customizable, extendable, framework-agnostic federated learning system supporting multiple machine learning frameworks.
Capability facts
- Languages
- python
Source: github.language · Aug 4, 2026
Categories
Tags
README
Flower: A Friendly Federated AI Framework
Flower (flwr) is a framework for building federated AI systems. The
design of Flower is based on a few guiding principles:
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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.
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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.
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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, CatBoost, LeRobot for federated robots, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand.
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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.
Stay tuned, more tutorials are coming soon. Topics include Privacy and Security in Federated Learning, and Scaling Federated Learning.
Documentation
- Installation
- Quickstart (TensorFlow)
- Quickstart (PyTorch)
- Quickstart (Hugging Face)
- Quickstart (PyTorch Lightning)
- [Quickstart (Pandas)](h
For agents
This page has a .md twin and JSON over the API.