Comparison
FLsystem-paper vs Awesome-AutoDL
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-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Markdown twin · FLsystem-paper alternatives · Awesome-AutoDL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FLsystem-paper | Awesome-AutoDL |
|---|---|---|
| Maintenance | Dormant (909d since push) As of 2w · github_public_v1 | Dormant (1408d 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-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
Stars
- FLsystem-paper
- 75
- Awesome-AutoDL
- 2.3k
Forks
- FLsystem-paper
- 7
- Awesome-AutoDL
- 319
Open issues
- FLsystem-paper
- 1
- Awesome-AutoDL
- 2
Language
- FLsystem-paper
- -
- Awesome-AutoDL
- Python
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-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Persona
- FLsystem-paper
- -
- Awesome-AutoDL
- -
Runtime
- FLsystem-paper
- -
- Awesome-AutoDL
- -
License
- FLsystem-paper
- (unknown)
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
Last pushed
- FLsystem-paper
- Feb 7, 2024
- Awesome-AutoDL
- Sep 26, 2022
Categories
- FLsystem-paper
- Developer Tools, Model Training
- Awesome-AutoDL
- Developer Tools, Model Training
Trust and health
Days since push
- FLsystem-paper
- 909d
- Awesome-AutoDL
- 1408d
Open issues (now)
- FLsystem-paper
- 1
- Awesome-AutoDL
- 2
Full report
- FLsystem-paper
- Trust report
- Awesome-AutoDL
- 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.
- 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-AutoDL if…
- Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning.
- Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
- More GitHub stars (2.3k vs 75) - visibility, not fit.
When NOT to use Awesome-AutoDL
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.
- Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
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 (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- 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: FLsystem-paper 75 · Awesome-AutoDL 2.3k (synced Aug 4, 2026).
Common questions
- What is the difference between FLsystem-paper and Awesome-AutoDL?
- FLsystem-paper: A curated list of FL system-related academic papers and frameworks. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories.
- When should I choose FLsystem-paper over Awesome-AutoDL?
- Choose FLsystem-paper over Awesome-AutoDL 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; 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-AutoDL over FLsystem-paper?
- Choose Awesome-AutoDL over FLsystem-paper when Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS); More GitHub stars (2.3k 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-AutoDL?
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
- Is FLsystem-paper or Awesome-AutoDL more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 75). Stars measure visibility, not whether either tool fits your constraints.
- Are FLsystem-paper and Awesome-AutoDL open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to FLsystem-paper or Awesome-AutoDL?
- GraphCanon lists graph-backed alternatives at FLsystem-paper alternatives and Awesome-AutoDL alternatives (FLsystem-paper markdown twin, Awesome-AutoDL 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-AutoDL?
- FLsystem-paper: Dormant. Awesome-AutoDL: Dormant. 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-AutoDL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FLsystem-paper trust report; Awesome-AutoDL trust report.