Comparison
FATE vs awesome-AutoML
Verdict
Pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · FATE alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FATE | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (623d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- FATE
- An Industrial Grade Federated Learning Framework
- awesome-AutoML
- Curating AutoML research and resources
Stars
- FATE
- 6.1k
- awesome-AutoML
- 941
Forks
- FATE
- 1.6k
- awesome-AutoML
- 156
Open issues
- FATE
- 21
- awesome-AutoML
- 1
Language
- FATE
- Python
- awesome-AutoML
- -
Adopt for
- FATE
- FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- FATE
- -
- awesome-AutoML
- -
Runtime
- FATE
- -
- awesome-AutoML
- -
License
- FATE
- Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users.
- awesome-AutoML
- GPL-3.0
Last pushed
- FATE
- Nov 19, 2024
- awesome-AutoML
- Mar 24, 2026
Categories
- FATE
- Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- FATE
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- FATE
- 623d
- awesome-AutoML
- 133d
Open issues (now)
- FATE
- 21
- awesome-AutoML
- 1
Owner type
- FATE
- Organization
- awesome-AutoML
- User
Full report
- FATE
- Trust report
- awesome-AutoML
- Trust report
Choose FATE if…
- License: FATE is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to FATE: algorithm, fate, federated-learning, machine-learning.
- When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information
When NOT to use FATE
- In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities
- If your project does not require federated learning's collaborative model training across disjoint data sets
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, FATE 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 (FederatedAI/FATE) · observed Aug 4, 2026
- GitHub forks (FederatedAI/FATE) · observed Aug 4, 2026
- Last push (FederatedAI/FATE) · observed Nov 19, 2024
- 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: FATE 6.1k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between FATE and awesome-AutoML?
- FATE: An Industrial Grade Federated Learning Framework. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose FATE over awesome-AutoML?
- Choose FATE over awesome-AutoML when License: FATE is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to FATE: algorithm, fate, federated-learning, machine-learning; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information.
- When should I choose awesome-AutoML over FATE?
- Choose awesome-AutoML over FATE when License: awesome-AutoML is GPL-3.0, FATE 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 FATE?
- In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities If your project does not require federated learning's collaborative model training across disjoint data sets
- 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 FATE or awesome-AutoML more popular on GitHub?
- FATE has more GitHub stars (6,089 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are FATE and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (FATE: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to FATE or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at FATE alternatives and awesome-AutoML alternatives (FATE 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, FATE or awesome-AutoML?
- FATE: Dormant. 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 FATE and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FATE trust report; awesome-AutoML trust report.