GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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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 Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
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 · Oct 3, 2026
Categories
Tags
README
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...
For agents
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