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

# Awesome-Federated-Learning vs awesome-federated-learning

*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 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-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. [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-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 2,017 | 738 |
| Forks | 332 | 98 |
| Open issues | 3 | 0 |
| Language | - | Shell |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. |
| Persona | - | - |
| Runtime | - | - |
| License | - | MIT |
| 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) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 1430d | 261d |
| Open issues (now) | 3 | 0 |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/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: 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-Federated-Learning if…

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

### Choose awesome-federated-learning if…

- Tags unique to awesome-federated-learning: data-privacy, machine-learning, non-iid, statistical-heterogeneity.
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL
- More recently updated (last pushed Nov 16, 2025).

## 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 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-Federated-Learning and awesome-federated-learning?

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. 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-Federated-Learning over awesome-federated-learning?

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

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

Choose awesome-federated-learning over Awesome-Federated-Learning when Tags unique to awesome-federated-learning: data-privacy, machine-learning, non-iid, statistical-heterogeneity; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL; More recently updated (last pushed Nov 16, 2025).

### 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 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-Federated-Learning or awesome-federated-learning more popular on GitHub?

Awesome-Federated-Learning has more GitHub stars (2,017 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and awesome-federated-learning open source?

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/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/chaoyanghe-awesome-federated-learning-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-Federated-Learning or awesome-federated-learning?

Awesome-Federated-Learning: 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-Federated-Learning and awesome-federated-learning?

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