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

# Awesome-Federated-Learning vs flower

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick Awesome-Federated-Learning if fedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency; pick flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.

[Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning) reports 2.0k GitHub stars, 332 forks, and 3 open issues, last pushed Sep 3, 2022. [flower](https://flower.ai) has 7.1k stars, 1.2k forks, and 368 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [flower's repository](https://github.com/flwrlabs/flower).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [flower](/tools/flwrlabs-flower.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | A Friendly Federated AI Framework |
| Stars | 2,017 | 7,067 |
| Forks | 332 | 1,214 |
| Open issues | 3 | 368 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| Categories | Evaluation & Observability, Model Training | Model Training |

## Trust and health

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

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [flower](/tools/flwrlabs-flower.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 1430d | 0d |
| Open issues (now) | 3 | 368 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/flwrlabs-flower/trust.md) |

## Decision facts: Awesome-Federated-Learning

- **Adopt for:** FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.

## Decision facts: flower

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

## Choose when

### Choose Awesome-Federated-Learning if…

- Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
- Also covers Evaluation & Observability.
- When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.

### Choose flower if…

- 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
- More GitHub stars (7.1k vs 2.0k) - visibility, not fit.

## When NOT to use Awesome-Federated-Learning

- If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity.
- When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.

## 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

## Common questions

### What is the difference between Awesome-Federated-Learning and flower?

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. flower: A Friendly Federated AI Framework. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Federated-Learning over flower?

Choose Awesome-Federated-Learning over flower when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; Also covers Evaluation & Observability; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.

### When should I choose flower over Awesome-Federated-Learning?

Choose flower over Awesome-Federated-Learning when 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; More GitHub stars (7.1k vs 2.0k) - visibility, not fit.

### When should I avoid Awesome-Federated-Learning?

If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity. When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.

### 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

### Is Awesome-Federated-Learning or flower more popular on GitHub?

flower has more GitHub stars (7,067 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and flower open source?

Yes - both are open-source projects on GitHub.

### Where can I find alternatives to Awesome-Federated-Learning or flower?

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [flower alternatives](/tools/flwrlabs-flower/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [flower markdown twin](/tools/flwrlabs-flower/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/chaoyanghe-awesome-federated-learning-vs-flwrlabs-flower.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-Federated-Learning or flower?

Awesome-Federated-Learning: Dormant. flower: Very active. 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 Awesome-Federated-Learning and flower?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning`](/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning)
- 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/_