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

# awesome-automl-papers vs awesome-federated-learning

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search; 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-automl-papers](https://github.com/hibayesian/awesome-automl-papers) reports 4.2k GitHub stars, 678 forks, and 2 open issues, last pushed Jun 11, 2024. [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-automl-papers's repository](https://github.com/hibayesian/awesome-automl-papers) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | A curated list of automated machine learning papers and resources. | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 4,155 | 738 |
| Forks | 678 | 98 |
| Open issues | 2 | 0 |
| Language | - | Shell |
| Adopt for | awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search. | 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 | Evaluation & Observability, Model Training | Model Training |

## Trust and health

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

| | [awesome-automl-papers](/tools/hibayesian-awesome-automl-papers.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 784d | 261d |
| Open issues (now) | 2 | 0 |
| Full report | [trust report](/tools/hibayesian-awesome-automl-papers/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) |

## Decision facts: awesome-automl-papers

- **Adopt for:** awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.

## 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-automl-papers if…

- License: awesome-automl-papers is Apache-2.0, awesome-federated-learning is MIT.
- Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search.
- Also covers Evaluation & Observability.
- When you need a curated list of academic materials to research or learn about AutoML technologies

### Choose awesome-federated-learning if…

- License: awesome-federated-learning is MIT, awesome-automl-papers is Apache-2.0.
- 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 NOT to use awesome-automl-papers

- If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
- When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers

## 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-automl-papers and awesome-federated-learning?

awesome-automl-papers: A curated list of automated machine learning papers and resources.. 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-automl-papers over awesome-federated-learning?

Choose awesome-automl-papers over awesome-federated-learning when License: awesome-automl-papers is Apache-2.0, awesome-federated-learning is MIT; Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; Also covers Evaluation & Observability; When you need a curated list of academic materials to research or learn about AutoML technologies.

### When should I choose awesome-federated-learning over awesome-automl-papers?

Choose awesome-federated-learning over awesome-automl-papers when License: awesome-federated-learning is MIT, awesome-automl-papers is Apache-2.0; 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 avoid awesome-automl-papers?

If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers

### 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-automl-papers or awesome-federated-learning more popular on GitHub?

awesome-automl-papers has more GitHub stars (4,155 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are awesome-automl-papers and awesome-federated-learning open source?

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

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

GraphCanon lists graph-backed alternatives at [awesome-automl-papers alternatives](/tools/hibayesian-awesome-automl-papers/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([awesome-automl-papers markdown twin](/tools/hibayesian-awesome-automl-papers/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/hibayesian-awesome-automl-papers-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-automl-papers or awesome-federated-learning?

awesome-automl-papers: 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-automl-papers and awesome-federated-learning?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers`](/api/graphcanon/graph?tool=hibayesian-awesome-automl-papers)
- 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/_