---
title: "flower vs awesome-federated-learning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/flwrlabs-flower-vs-weimingwill-awesome-federated-learning"
tools: ["flwrlabs-flower", "weimingwill-awesome-federated-learning"]
---

# flower vs awesome-federated-learning

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python; pick awesome-federated-learning if awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.

[flower](https://flower.ai) reports 7.1k GitHub stars, 1.2k forks, and 368 open issues, last pushed Aug 4, 2026. [awesome-federated-learning](https://github.com/EasyFL-AI/EasyFL) has 738 stars, 98 forks, and 0 open issues, last pushed Nov 16, 2025. Figures are from public GitHub metadata via [flower's repository](https://github.com/flwrlabs/flower) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [flower](/tools/flwrlabs-flower.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | A Friendly Federated AI Framework | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 7,067 | 738 |
| Forks | 1,214 | 98 |
| Open issues | 368 | 0 |
| Language | Python | Shell |
| Adopt for | A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python. | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. |
| Persona | - | - |
| Runtime | - | - |
| License | Apache-2.0 | MIT |
| Categories | Model Training | Model Training |

## Trust and health

_Sourced signals - not a safety guarantee. No winner column._

| | [flower](/tools/flwrlabs-flower.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 261d |
| Open issues (now) | 368 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/flwrlabs-flower/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) |

## Decision facts: flower

- **Adopt for:** A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.

## Decision facts: awesome-federated-learning

- **Adopt for:** awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.

## Choose when

### Choose flower if…

- flower is primarily Python; awesome-federated-learning is Shell.
- License: flower is Apache-2.0, awesome-federated-learning is MIT.
- Tags unique to flower: ai-frameworks, python, pytorch, tensorflow.
- When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning

### Choose awesome-federated-learning if…

- awesome-federated-learning is primarily Shell; flower is Python.
- License: awesome-federated-learning is MIT, flower is Apache-2.0.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid.
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL

## When NOT to use flower

- 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

## When NOT to use awesome-federated-learning

- Avoid if your project does not require federated learning-specific optimizations or frameworks
- Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL

## Common questions

### What is the difference between flower and awesome-federated-learning?

flower: A Friendly Federated AI Framework. awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. See the comparison table for live GitHub stats and shared categories.

### When should I choose flower over awesome-federated-learning?

Choose flower over awesome-federated-learning when flower is primarily Python; awesome-federated-learning is Shell; License: flower is Apache-2.0, awesome-federated-learning is MIT; Tags unique to flower: ai-frameworks, python, pytorch, tensorflow; When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning.

### When should I choose awesome-federated-learning over flower?

Choose awesome-federated-learning over flower when awesome-federated-learning is primarily Shell; flower is Python; License: awesome-federated-learning is MIT, flower is Apache-2.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.

### When should I avoid flower?

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

### When should I avoid awesome-federated-learning?

Avoid if your project does not require federated learning-specific optimizations or frameworks Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL

### Is flower or awesome-federated-learning more popular on GitHub?

flower has more GitHub stars (7,067 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are flower and awesome-federated-learning open source?

Yes - both are open-source projects on GitHub (flower: Apache-2.0, awesome-federated-learning: MIT).

### Where can I find alternatives to flower or awesome-federated-learning?

GraphCanon lists graph-backed alternatives at [flower alternatives](/tools/flwrlabs-flower/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([flower markdown twin](/tools/flwrlabs-flower/alternatives.md), [awesome-federated-learning markdown twin](/tools/weimingwill-awesome-federated-learning/alternatives.md)), ranked by typed relationship edges rather than popularity votes.

### Is there a machine-readable version of this comparison?

Yes. The markdown twin at [this comparison](/compare/flwrlabs-flower-vs-weimingwill-awesome-federated-learning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, flower or awesome-federated-learning?

flower: Very active. awesome-federated-learning: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.

### Where are the full trust reports for flower and awesome-federated-learning?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [flower trust report](/tools/flwrlabs-flower/trust); [awesome-federated-learning trust report](/tools/weimingwill-awesome-federated-learning/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=flwrlabs-flower`](/api/graphcanon/graph?tool=flwrlabs-flower)
- LLM index: [/llms.txt](/llms.txt)
- Full corpus: [/llms-full.txt](/llms-full.txt)

_GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_