Comparison
pytorch-meta vs awesome-federated-learning
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.
Markdown twin · pytorch-meta alternatives · awesome-federated-learning alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | pytorch-meta | awesome-federated-learning |
|---|---|---|
| Maintenance | Dormant (1113d since push) As of 3w · github_public_v1 | Slowing (261d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 3w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- pytorch-meta
- 2.1k
- awesome-federated-learning
- 738
Forks
- pytorch-meta
- 264
- awesome-federated-learning
- 98
Open issues
- pytorch-meta
- 61
- awesome-federated-learning
- 0
Language
- pytorch-meta
- Python
- awesome-federated-learning
- Shell
Adopt for
- pytorch-meta
- 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
- awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
Persona
- pytorch-meta
- -
- awesome-federated-learning
- -
Runtime
- pytorch-meta
- -
- awesome-federated-learning
- -
License
- pytorch-meta
- MIT
- awesome-federated-learning
- MIT
Last pushed
- pytorch-meta
- Jul 17, 2023
- awesome-federated-learning
- Nov 16, 2025
Categories
- pytorch-meta
- Model Training
- awesome-federated-learning
- Model Training
Trust and health
Maintenance
- pytorch-meta
- Dormant (18%)
- awesome-federated-learning
- Slowing (36%)
Days since push
- pytorch-meta
- 1113d
- awesome-federated-learning
- 261d
Open issues (now)
- pytorch-meta
- 61
- awesome-federated-learning
- 0
Full report
- pytorch-meta
- Trust report
- awesome-federated-learning
- Trust report
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.
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.
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 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
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (tristandeleu/pytorch-meta) · observed Aug 4, 2026
- GitHub forks (tristandeleu/pytorch-meta) · observed Aug 4, 2026
- Last push (tristandeleu/pytorch-meta) · observed Jul 17, 2023
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (weimingwill/awesome-federated-learning) · observed Aug 4, 2026
- GitHub forks (weimingwill/awesome-federated-learning) · observed Aug 4, 2026
- Last push (weimingwill/awesome-federated-learning) · observed Nov 16, 2025
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: pytorch-meta 2.1k · awesome-federated-learning 738 (synced Aug 4, 2026).
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 and awesome-federated-learning alternatives (pytorch-meta markdown twin, awesome-federated-learning markdown twin), 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 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; awesome-federated-learning trust report.