---
title: "pytorch-meta vs awesome-federated-learning"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/tristandeleu-pytorch-meta-vs-weimingwill-awesome-federated-learning"
tools: ["tristandeleu-pytorch-meta", "weimingwill-awesome-federated-learning"]
---

# pytorch-meta vs awesome-federated-learning

*GraphCanon updated Aug 4, 2026*

## Verdict

Pick pytorch-meta if pyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks; 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.

[pytorch-meta](https://tristandeleu.github.io/pytorch-meta/) reports 2.1k GitHub stars, 264 forks, and 61 open issues, last pushed Jul 17, 2023. [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 [pytorch-meta's repository](https://github.com/tristandeleu/pytorch-meta) and [awesome-federated-learning's repository](https://github.com/weimingwill/awesome-federated-learning).

| | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Tagline | Extensions and data-loaders for few-shot learning & meta-learning in PyTorch | Curated federated learning resources including papers, blogs, videos, and projects |
| Stars | 2,062 | 738 |
| Forks | 264 | 98 |
| Open issues | 61 | 0 |
| Language | Python | Shell |
| Adopt for | PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks. | awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT | MIT |
| Categories | Model Training | Model Training |

## Trust and health

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

| | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | [awesome-federated-learning](/tools/weimingwill-awesome-federated-learning.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Slowing (36%) |
| Days since push | 1113d | 261d |
| Open issues (now) | 61 | 0 |
| Full report | [trust report](/tools/tristandeleu-pytorch-meta/trust.md) | [trust report](/tools/weimingwill-awesome-federated-learning/trust.md) |

## Decision facts: pytorch-meta

- **Adopt for:** PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks.

## 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 pytorch-meta if…

- pytorch-meta is primarily Python; awesome-federated-learning is Shell.
- Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning.
- When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available.

### Choose awesome-federated-learning if…

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

## When NOT to use pytorch-meta

- If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning.
- For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4.

## 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 pytorch-meta and awesome-federated-learning?

pytorch-meta: Extensions and data-loaders for few-shot learning & meta-learning in PyTorch. 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 pytorch-meta over awesome-federated-learning?

Choose pytorch-meta over awesome-federated-learning when pytorch-meta is primarily Python; awesome-federated-learning is Shell; Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning; When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available.

### When should I choose awesome-federated-learning over pytorch-meta?

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

### When should I avoid pytorch-meta?

If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning. For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4.

### 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 pytorch-meta or awesome-federated-learning more popular on GitHub?

pytorch-meta has more GitHub stars (2,062 vs 738). Stars measure visibility, not whether either tool fits your constraints.

### Are pytorch-meta and awesome-federated-learning open source?

Yes - both are open-source projects on GitHub (pytorch-meta: MIT, awesome-federated-learning: MIT).

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

GraphCanon lists graph-backed alternatives at [pytorch-meta alternatives](/tools/tristandeleu-pytorch-meta/alternatives) and [awesome-federated-learning alternatives](/tools/weimingwill-awesome-federated-learning/alternatives) ([pytorch-meta markdown twin](/tools/tristandeleu-pytorch-meta/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/tristandeleu-pytorch-meta-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, pytorch-meta or awesome-federated-learning?

pytorch-meta: 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 pytorch-meta and awesome-federated-learning?

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

---

**Machine-readable endpoints**

- JSON: [`/api/graphcanon/graph?tool=tristandeleu-pytorch-meta`](/api/graphcanon/graph?tool=tristandeleu-pytorch-meta)
- 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/_