Comparison
tensorflow-federated vs awesome-AutoML
Verdict
Pick tensorflow-federated if tensorFlow Federated enables decentralized machine learning and computations without sharing raw data; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · tensorflow-federated alternatives · awesome-AutoML alternatives
GraphCanon updated 3w
vs
Trust & integrity
| Signal | tensorflow-federated | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 3w · github_public_v1 | Slowing (133d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- tensorflow-federated
- An open-source framework for machine learning and other computations on decentralized data
- awesome-AutoML
- Curating AutoML research and resources
Stars
- tensorflow-federated
- 2.4k
- awesome-AutoML
- 941
Forks
- tensorflow-federated
- 604
- awesome-AutoML
- 156
Open issues
- tensorflow-federated
- 290
- awesome-AutoML
- 1
Language
- tensorflow-federated
- Python
- awesome-AutoML
- -
Adopt for
- tensorflow-federated
- TensorFlow Federated enables decentralized machine learning and computations without sharing raw data.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- tensorflow-federated
- -
- awesome-AutoML
- -
Runtime
- tensorflow-federated
- -
- awesome-AutoML
- -
License
- tensorflow-federated
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- tensorflow-federated
- Aug 3, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- tensorflow-federated
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- tensorflow-federated
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- tensorflow-federated
- 0d
- awesome-AutoML
- 133d
Open issues (now)
- tensorflow-federated
- 290
- awesome-AutoML
- 1
Owner type
- tensorflow-federated
- Organization
- awesome-AutoML
- User
Full report
- tensorflow-federated
- Trust report
- awesome-AutoML
- Trust report
Choose tensorflow-federated if…
- License: tensorflow-federated is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to tensorflow-federated: decentralized data, federated-learning, 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 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.
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, tensorflow-federated is Apache-2.0.
- 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 (google-parfait/tensorflow-federated) · observed Aug 4, 2026
- GitHub forks (google-parfait/tensorflow-federated) · observed Aug 4, 2026
- Last push (google-parfait/tensorflow-federated) · observed Aug 3, 2026
- License file (Apache-2.0) · 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: tensorflow-federated 2.4k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between tensorflow-federated and awesome-AutoML?
- tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose tensorflow-federated over awesome-AutoML?
- Choose tensorflow-federated over awesome-AutoML when License: tensorflow-federated is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to tensorflow-federated: decentralized data, federated-learning, 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-AutoML over tensorflow-federated?
- Choose awesome-AutoML over tensorflow-federated when License: awesome-AutoML is GPL-3.0, tensorflow-federated is Apache-2.0; 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 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-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 tensorflow-federated or awesome-AutoML more popular on GitHub?
- tensorflow-federated has more GitHub stars (2,445 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are tensorflow-federated and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (tensorflow-federated: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to tensorflow-federated or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at tensorflow-federated alternatives and awesome-AutoML alternatives (tensorflow-federated 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, tensorflow-federated or awesome-AutoML?
- tensorflow-federated: Very active. 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 tensorflow-federated and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: tensorflow-federated trust report; awesome-AutoML trust report.