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

# Awesome-Federated-Learning vs awesome-mlops

*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-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.

[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-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | A curated list of awesome MLOps tools. |
| Stars | 2,017 | 5,229 |
| Forks | 332 | 762 |
| Open issues | 3 | 71 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. |
| Persona | - | - |
| Runtime | - | - |
| License | - | - |
| Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training |

## Trust and health

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

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 1430d | 97d |
| Open issues (now) | 3 | 71 |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/kelvins-awesome-mlops/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-mlops

- **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.

## Choose when

### Choose Awesome-Federated-Learning if…

- Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
- When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.
- Leaner open-issue backlog (3).

### Choose awesome-mlops if…

- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Developer Tools, Inference & Serving.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

## 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-mlops

- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.

## Common questions

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

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.

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

Choose Awesome-Federated-Learning over awesome-mlops when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches; Leaner open-issue backlog (3).

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

Choose awesome-mlops over Awesome-Federated-Learning when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.

### 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-mlops?

In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.

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

awesome-mlops has more GitHub stars (5,229 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and awesome-mlops open source?

Yes - both are open-source projects on GitHub.

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

GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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-kelvins-awesome-mlops.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-mlops?

Awesome-Federated-Learning: Dormant. awesome-mlops: 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-mlops?

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-mlops trust report](/tools/kelvins-awesome-mlops/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/_