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

# Awesome-Federated-Learning vs tensorflow-federated

*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 tensorflow-federated if tensorFlow Federated enables decentralized machine learning and computations without sharing raw data.

[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. [tensorflow-federated](https://github.com/google-parfait/tensorflow-federated) has 2.4k stars, 604 forks, and 290 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [tensorflow-federated's repository](https://github.com/google-parfait/tensorflow-federated).

| | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) |
| --- | --- | --- |
| Tagline | FedML - The Research and Production Integrated Federated Learning Library | An open-source framework for machine learning and other computations on decentralized data |
| Stars | 2,017 | 2,445 |
| Forks | 332 | 604 |
| Open issues | 3 | 290 |
| Language | - | Python |
| Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. |
| Persona | - | - |
| Runtime | - | - |
| License | - | Apache-2.0 |
| 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) | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Very active (96%) |
| Days since push | 1430d | 0d |
| Open issues (now) | 3 | 290 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/google-parfait-tensorflow-federated/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: tensorflow-federated

- **Adopt for:** TensorFlow Federated enables decentralized machine learning and computations without sharing raw data.

## 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 tensorflow-federated if…

- Tags unique to tensorflow-federated: decentralized data, tensorflow.
- If you need to develop federated learning algorithms that can train models across multiple devices or servers while keeping the training data distributed and secure.
- More GitHub stars (2.4k 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 tensorflow-federated

- Avoid if you require centralized data for your learning models, as TensorFlow Federated's strength lies in its capabilities to maintain decentralized datasets.
- If real-time computation or very low latency requirements are critical to your project; the nature of federated learning involves significant overhead and does not perform well in such scenarios.

## Common questions

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

Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data. See the comparison table for live GitHub stats and shared categories.

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

Choose Awesome-Federated-Learning over tensorflow-federated 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 tensorflow-federated over Awesome-Federated-Learning?

Choose tensorflow-federated over Awesome-Federated-Learning when Tags unique to tensorflow-federated: decentralized data, tensorflow; If you need to develop federated learning algorithms that can train models across multiple devices or servers while keeping the training data distributed and secure; More GitHub stars (2.4k 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 tensorflow-federated?

Avoid if you require centralized data for your learning models, as TensorFlow Federated's strength lies in its capabilities to maintain decentralized datasets. If real-time computation or very low latency requirements are critical to your project; the nature of federated learning involves significant overhead and does not perform well in such scenarios.

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

tensorflow-federated has more GitHub stars (2,445 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-Federated-Learning and tensorflow-federated open source?

Yes - both are open-source projects on GitHub.

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

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

Awesome-Federated-Learning: Dormant. tensorflow-federated: Very active. 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 tensorflow-federated?

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