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

# tensorflow-federated vs awesome-federated-learning

*GraphCanon updated Aug 4, 2026*

## Verdict

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

[tensorflow-federated](https://github.com/google-parfait/tensorflow-federated) reports 2.4k GitHub stars, 604 forks, and 290 open issues, last pushed Aug 3, 2026. [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 [tensorflow-federated's repository](https://github.com/google-parfait/tensorflow-federated) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | An open-source framework for machine learning and other computations on decentralized data | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 2,445 | 738 |
| Forks | 604 | 98 |
| Open issues | 290 | 0 |
| Language | Python | Shell |
| Adopt for | TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. | 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 | Model Training | Model Training |

## Trust and health

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

| | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Very active (96%) | Slowing (36%) |
| Days since push | 0d | 261d |
| Open issues (now) | 290 | 0 |
| Owner type | Organization | User |
| Full report | [trust report](/tools/google-parfait-tensorflow-federated/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) |

## Decision facts: tensorflow-federated

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

## 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 tensorflow-federated if…

- tensorflow-federated is primarily Python; awesome-federated-learning is Shell.
- License: tensorflow-federated is Apache-2.0, awesome-federated-learning is MIT.
- 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.

### Choose awesome-federated-learning if…

- awesome-federated-learning is primarily Shell; tensorflow-federated is Python.
- License: awesome-federated-learning is MIT, tensorflow-federated is Apache-2.0.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid.
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL

## 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.

## 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 tensorflow-federated and awesome-federated-learning?

tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data. 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 tensorflow-federated over awesome-federated-learning?

Choose tensorflow-federated over awesome-federated-learning when tensorflow-federated is primarily Python; awesome-federated-learning is Shell; License: tensorflow-federated is Apache-2.0, awesome-federated-learning is MIT; 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.

### When should I choose awesome-federated-learning over tensorflow-federated?

Choose awesome-federated-learning over tensorflow-federated when awesome-federated-learning is primarily Shell; tensorflow-federated is Python; License: awesome-federated-learning is MIT, tensorflow-federated is Apache-2.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.

### 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.

### 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 tensorflow-federated or awesome-federated-learning more popular on GitHub?

tensorflow-federated has more GitHub stars (2,445 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are tensorflow-federated and awesome-federated-learning open source?

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

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

GraphCanon lists graph-backed alternatives at [tensorflow-federated alternatives](/tools/google-parfait-tensorflow-federated/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([tensorflow-federated markdown twin](/tools/google-parfait-tensorflow-federated/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/google-parfait-tensorflow-federated-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, tensorflow-federated or awesome-federated-learning?

tensorflow-federated: Very active. 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 tensorflow-federated and awesome-federated-learning?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=google-parfait-tensorflow-federated`](/api/graphcanon/graph?tool=google-parfait-tensorflow-federated)
- 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/_