Comparison
FLsystem-paper vs awesome-AutoML
Verdict
Pick FLsystem-paper if fLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · FLsystem-paper alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FLsystem-paper | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (909d 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
- FLsystem-paper
- A curated list of FL system-related academic papers and frameworks
- awesome-AutoML
- Curating AutoML research and resources
Stars
- FLsystem-paper
- 75
- awesome-AutoML
- 941
Forks
- FLsystem-paper
- 7
- awesome-AutoML
- 156
Open issues
- FLsystem-paper
- 1
- awesome-AutoML
- 1
Language
- FLsystem-paper
- -
- awesome-AutoML
- -
Adopt for
- FLsystem-paper
- FLsystem-paper is a curated list of federated learning systems literature geared towards providing research and development insights exclusively from big tech companies and open-source projects.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- FLsystem-paper
- -
- awesome-AutoML
- -
Runtime
- FLsystem-paper
- -
- awesome-AutoML
- -
License
- FLsystem-paper
- (unknown)
- awesome-AutoML
- GPL-3.0
Last pushed
- FLsystem-paper
- Feb 7, 2024
- awesome-AutoML
- Mar 24, 2026
Categories
- FLsystem-paper
- Developer Tools, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- FLsystem-paper
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- FLsystem-paper
- 909d
- awesome-AutoML
- 133d
Full report
- FLsystem-paper
- Trust report
- awesome-AutoML
- Trust report
Choose FLsystem-paper if…
- (no information available)
- Pricing: The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models..
- Tags unique to FLsystem-paper: federated-learning, machine-learning, papers.
- Also covers Developer Tools.
- When you need to focus on federated learning systems contributions from major technology firms like Apple, Google, Meta, Microsoft, IBM, Nvidia, WeBank, and Alibaba.
When NOT to use FLsystem-paper
- If your research scope is broader than federated learning systems; this repository focuses specifically on the system aspects within FL.
- For a comprehensive collection that includes other ML domains, as FLsystem-paper restricts its curation to federated learning systems and closely related works.
Choose awesome-AutoML if…
- 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.
- More GitHub stars (941 vs 75) - visibility, not fit.
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 (AmberLJC/FLsystem-paper) · observed Aug 4, 2026
- GitHub forks (AmberLJC/FLsystem-paper) · observed Aug 4, 2026
- Last push (AmberLJC/FLsystem-paper) · observed Feb 7, 2024
- License file (unknown) · 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: FLsystem-paper 75 · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between FLsystem-paper and awesome-AutoML?
- FLsystem-paper: A curated list of FL system-related academic papers and frameworks. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose FLsystem-paper over awesome-AutoML?
- Choose FLsystem-paper over awesome-AutoML when (no information available); Pricing: The repository itself is free and open source, but usage might involve proprietary frameworks or projects from big tech companies that could have their own licensing models.; Tags unique to FLsystem-paper: federated-learning, machine-learning, papers; Also covers Developer Tools; When you need to focus on federated learning systems contributions from major technology firms like Apple, Google, Meta, Microsoft, IBM, Nvidia, WeBank, and Alibaba.
- When should I choose awesome-AutoML over FLsystem-paper?
- Choose awesome-AutoML over FLsystem-paper when 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; More GitHub stars (941 vs 75) - visibility, not fit.
- When should I avoid FLsystem-paper?
- If your research scope is broader than federated learning systems; this repository focuses specifically on the system aspects within FL. For a comprehensive collection that includes other ML domains, as FLsystem-paper restricts its curation to federated learning systems and closely related works.
- 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 FLsystem-paper or awesome-AutoML more popular on GitHub?
- awesome-AutoML has more GitHub stars (941 vs 75). Stars measure visibility, not whether either tool fits your constraints.
- Are FLsystem-paper and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to FLsystem-paper or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at FLsystem-paper alternatives and awesome-AutoML alternatives (FLsystem-paper 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, FLsystem-paper or awesome-AutoML?
- FLsystem-paper: 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 FLsystem-paper and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FLsystem-paper trust report; awesome-AutoML trust report.