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

# Awesome-Federated-Learning vs horovod

*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 horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.

[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. [horovod](http://horovod.ai) has 15k stars, 2.2k forks, and 406 open issues, last pushed Jul 29, 2026. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [horovod's repository](https://github.com/horovod/horovod).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [horovod](/tools/horovod-horovod.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. |
| Stars | 2,017 | 14,695 |
| Forks | 332 | 2,235 |
| Open issues | 3 | 406 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Other |
| 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) | [horovod](/tools/horovod-horovod.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Archived (8%) |
| Days since push | 1430d | 4d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 3 | 406 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/horovod-horovod/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: horovod

- **Adopt for:** Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.

## Choose when

### Choose Awesome-Federated-Learning if…

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

### Choose horovod if…

- Tags unique to horovod: deep-learning, distributed-training, keras, mxnet.
- When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
- More GitHub stars (15k vs 2.0k) - visibility, not fit.

## 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 horovod

- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility.
- Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.

## Common questions

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

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-Federated-Learning over horovod?

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

### When should I choose horovod over Awesome-Federated-Learning?

Choose horovod over Awesome-Federated-Learning when Tags unique to horovod: deep-learning, distributed-training, keras, mxnet; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts; More GitHub stars (15k vs 2.0k) - visibility, not fit.

### 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 horovod?

Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.

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

horovod has more GitHub stars (14,695 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and horovod open source?

Yes - both are open-source projects on GitHub.

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

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

Awesome-Federated-Learning: Dormant. horovod: Archived. 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 horovod?

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