---
title: "Awesome-AutoDL vs awesome-federated-learning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-weimingwill-awesome-federated-learning"
tools: ["d-x-y-awesome-autodl", "weimingwill-awesome-federated-learning"]
---

# Awesome-AutoDL vs awesome-federated-learning

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; 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.

[Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. [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 [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 2,339 | 738 |
| Forks | 319 | 98 |
| Open issues | 2 | 0 |
| Language | Python | Shell |
| Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | MIT |
| Categories | Developer Tools, Model Training | Model Training |

## Trust and health

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

| | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 1408d | 261d |
| Open issues (now) | 2 | 0 |
| Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) |

## Decision facts: Awesome-AutoDL

- **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.

## 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 Awesome-AutoDL if…

- Awesome-AutoDL is primarily Python; awesome-federated-learning is Shell.
- Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning.
- Also covers Developer Tools.
- Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).

### Choose awesome-federated-learning if…

- awesome-federated-learning is primarily Shell; Awesome-AutoDL is Python.
- 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 NOT to use Awesome-AutoDL

- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.
- Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.

## 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 Awesome-AutoDL and awesome-federated-learning?

Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. 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 Awesome-AutoDL over awesome-federated-learning?

Choose Awesome-AutoDL over awesome-federated-learning when Awesome-AutoDL is primarily Python; awesome-federated-learning is Shell; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).

### When should I choose awesome-federated-learning over Awesome-AutoDL?

Choose awesome-federated-learning over Awesome-AutoDL when awesome-federated-learning is primarily Shell; Awesome-AutoDL is Python; 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 avoid Awesome-AutoDL?

Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.

### 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 Awesome-AutoDL or awesome-federated-learning more popular on GitHub?

Awesome-AutoDL has more GitHub stars (2,339 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-AutoDL and awesome-federated-learning open source?

Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, awesome-federated-learning: MIT).

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

GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/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/d-x-y-awesome-autodl-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, Awesome-AutoDL or awesome-federated-learning?

Awesome-AutoDL: Dormant. 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 Awesome-AutoDL and awesome-federated-learning?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [awesome-federated-learning trust report](/tools/weimingwill-awesome-federated-learning/trust).

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl)
- 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/_