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

# awesome-federated-learning vs awesome-AutoML

*GraphCanon updated Aug 4, 2026*

## Verdict

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; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.

[awesome-federated-learning](https://github.com/EasyFL-AI/EasyFL) reports 738 GitHub stars, 98 forks, and 0 open issues, last pushed Nov 16, 2025. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML).

| | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) |
| --- | --- | --- |
| Tagline | Curated federated learning resources including papers, blogs, videos, and projects | Curating AutoML research and resources |
| Stars | 738 | 941 |
| Forks | 98 | 156 |
| Open issues | 0 | 1 |
| Language | Shell | - |
| Adopt for | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | GPL-3.0 |
| Categories | Model Training | Model Training |

## Trust and health

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

| | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) |
| --- | --- | --- |
| Days since push | 261d | 133d |
| Open issues (now) | 0 | 1 |
| Full report | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) |

## 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.

## Decision facts: awesome-AutoML

- **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.

## Choose when

### Choose awesome-federated-learning if…

- License: awesome-federated-learning is MIT, awesome-AutoML is GPL-3.0.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, federated-learning, machine-learning.
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL

### Choose awesome-AutoML if…

- License: awesome-AutoML is GPL-3.0, awesome-federated-learning is MIT.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.

## 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

## When NOT to use awesome-AutoML

- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.

## Common questions

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

awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.

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

Choose awesome-federated-learning over awesome-AutoML when License: awesome-federated-learning is MIT, awesome-AutoML is GPL-3.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, federated-learning, machine-learning; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.

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

Choose awesome-AutoML over awesome-federated-learning when License: awesome-AutoML is GPL-3.0, awesome-federated-learning is MIT; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.

### 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

### When should I avoid awesome-AutoML?

If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.

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

awesome-AutoML has more GitHub stars (941 vs 738). Stars measure visibility, not whether either tool fits your constraints.

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

Yes - both are open-source projects on GitHub (awesome-federated-learning: MIT, awesome-AutoML: GPL-3.0).

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

GraphCanon lists graph-backed alternatives at [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([awesome-federated-learning markdown twin](/tools/weimingwill-awesome-federated-learning/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/weimingwill-awesome-federated-learning-vs-windmaple-awesome-automl.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 awesome-AutoML?

awesome-federated-learning: Slowing. awesome-AutoML: 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 awesome-federated-learning and awesome-AutoML?

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

---

**Machine-readable endpoints**

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