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Home/Compare/Awesome-Federated-Learning vs awesome-automl-papers

Comparison

Awesome-Federated-Learning vs awesome-automl-papers

Verdict

Pick Awesome-Federated-Learning if fedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency; pick awesome-automl-papers if awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.

Markdown twin · Awesome-Federated-Learning alternatives · awesome-automl-papers alternatives

GraphCanon updated 2w

Awesome-Federated-Learning logo

Awesome-Federated-Learning

chaoyanghe/Awesome-Federated-Learning

★ 2.0kpushed Sep 3, 2022
vs
awesome-automl-papers logo

awesome-automl-papers

hibayesian/awesome-automl-papers

★ 4.2kpushed Jun 11, 2024

Trust & integrity

SignalAwesome-Federated-Learningawesome-automl-papers
Maintenance
Dormant (1430d since push)
As of 2w · github_public_v1
Dormant (784d 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
Awesome-Federated-Learning trust reportawesome-automl-papers trust reportTrust methodology

Tagline

Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library
awesome-automl-papers
A curated list of automated machine learning papers and resources.

Stars

Awesome-Federated-Learning
2.0k
awesome-automl-papers
4.2k

Forks

Awesome-Federated-Learning
332
awesome-automl-papers
678

Open issues

Awesome-Federated-Learning
3
awesome-automl-papers
2

Language

Awesome-Federated-Learning
-
awesome-automl-papers
-

Adopt for

Awesome-Federated-Learning
FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
awesome-automl-papers
awesome-automl-papers is an organized collection of AutoML academic resources including papers on automated feature engineering, hyperparameter optimization, and neural architecture search.

Persona

Awesome-Federated-Learning
-
awesome-automl-papers
-

Runtime

Awesome-Federated-Learning
-
awesome-automl-papers
-

License

Awesome-Federated-Learning
-
awesome-automl-papers
Apache-2.0

Last pushed

Awesome-Federated-Learning
Sep 3, 2022
awesome-automl-papers
Jun 11, 2024

Categories

Awesome-Federated-Learning
Evaluation & Observability, Model Training
awesome-automl-papers
Evaluation & Observability, Model Training

Trust and health

Days since push

Awesome-Federated-Learning
1430d
awesome-automl-papers
784d

Open issues (now)

Awesome-Federated-Learning
3
awesome-automl-papers
2

Full report

Awesome-Federated-Learning
Trust report
awesome-automl-papers
Trust report

Choose Awesome-Federated-Learning if…

  • Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
  • When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.

When NOT to use Awesome-Federated-Learning

  • If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity.
  • When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.
GitHubDetails

Choose awesome-automl-papers if…

  • Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search.
  • When you need a curated list of academic materials to research or learn about AutoML technologies
  • More GitHub stars (4.2k vs 2.0k) - visibility, not fit.

When NOT to use awesome-automl-papers

  • If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources
  • When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
GitHubDetails

Explore

Awesome-Federated-Learning trust report →awesome-automl-papers trust report →Evaluation & Observability category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: Awesome-Federated-Learning 2.0k · awesome-automl-papers 4.2k (synced Aug 4, 2026).

Common questions

What is the difference between Awesome-Federated-Learning and awesome-automl-papers?
Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. awesome-automl-papers: A curated list of automated machine learning papers and resources.. See the comparison table for live GitHub stats and shared categories.
When should I choose Awesome-Federated-Learning over awesome-automl-papers?
Choose Awesome-Federated-Learning over awesome-automl-papers when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.
When should I choose awesome-automl-papers over Awesome-Federated-Learning?
Choose awesome-automl-papers over Awesome-Federated-Learning when Tags unique to awesome-automl-papers: automl, feature-engineering, hyperparameter-optimization, neural-architecture-search; When you need a curated list of academic materials to research or learn about AutoML technologies; More GitHub stars (4.2k vs 2.0k) - visibility, not fit.
When should I avoid Awesome-Federated-Learning?
If your project does not benefit from extensive research integration, as this library might introduce unnecessary complexity. When the specific licensing details of FedML are uncertain or unaligned with the project's requirements.
When should I avoid awesome-automl-papers?
If looking for direct integration with commercial AutoML systems, as the tool provides only a list of academic papers and resources When seeking practical AutoML solutions to directly apply in production settings without extensive customization or interpretation from papers
Is Awesome-Federated-Learning or awesome-automl-papers more popular on GitHub?
awesome-automl-papers has more GitHub stars (4,155 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.
Are Awesome-Federated-Learning and awesome-automl-papers open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to Awesome-Federated-Learning or awesome-automl-papers?
GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and awesome-automl-papers alternatives (Awesome-Federated-Learning markdown twin, awesome-automl-papers 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-papers?
Awesome-Federated-Learning: Dormant. awesome-automl-papers: 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 Awesome-Federated-Learning and awesome-automl-papers?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; awesome-automl-papers trust report.

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