Comparison
awesome-federated-learning vs awesome-AutoML
Verdict
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; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · awesome-federated-learning alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-federated-learning | awesome-AutoML |
|---|---|---|
| Maintenance | Slowing (261d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · 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
- awesome-federated-learning
- Curated federated learning resources including papers, blogs, videos, and projects
- awesome-AutoML
- Curating AutoML research and resources
Stars
- awesome-federated-learning
- 738
- awesome-AutoML
- 941
Forks
- awesome-federated-learning
- 98
- awesome-AutoML
- 156
Open issues
- awesome-federated-learning
- 0
- awesome-AutoML
- 1
Language
- awesome-federated-learning
- Shell
- awesome-AutoML
- -
Adopt for
- awesome-federated-learning
- awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- awesome-federated-learning
- -
- awesome-AutoML
- -
Runtime
- awesome-federated-learning
- -
- awesome-AutoML
- -
License
- awesome-federated-learning
- MIT
- awesome-AutoML
- GPL-3.0
Last pushed
- awesome-federated-learning
- Nov 16, 2025
- awesome-AutoML
- Mar 24, 2026
Categories
- awesome-federated-learning
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Days since push
- awesome-federated-learning
- 261d
- awesome-AutoML
- 133d
Open issues (now)
- awesome-federated-learning
- 0
- awesome-AutoML
- 1
Full report
- awesome-federated-learning
- Trust report
- awesome-AutoML
- Trust report
Choose awesome-federated-learning if…
- License: awesome-federated-learning is MIT, awesome-AutoML is GPL-3.0.
- 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
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, awesome-federated-learning is MIT.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-federated-learning 738 · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-federated-learning and awesome-AutoML?
- awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-federated-learning over awesome-AutoML?
- Choose awesome-federated-learning over awesome-AutoML when License: awesome-federated-learning is MIT, awesome-AutoML is GPL-3.0; 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 choose awesome-AutoML over awesome-federated-learning?
- Choose awesome-AutoML over awesome-federated-learning when License: awesome-AutoML is GPL-3.0, awesome-federated-learning is MIT; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- 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
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is awesome-federated-learning or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 738). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-federated-learning and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (awesome-federated-learning: MIT, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to awesome-federated-learning or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at awesome-federated-learning alternatives and awesome-AutoML alternatives (awesome-federated-learning markdown twin, awesome-AutoML 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, awesome-federated-learning or awesome-AutoML?
- awesome-federated-learning: Slowing. awesome-AutoML: 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 awesome-federated-learning and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-federated-learning trust report; awesome-AutoML trust report.