Comparison
Awesome-Federated-Learning vs awesome-mlops
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-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Markdown twin · Awesome-Federated-Learning alternatives · awesome-mlops alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | awesome-mlops |
|---|---|---|
| Maintenance | Dormant (1430d since push) As of 2w · github_public_v1 | Slowing (97d 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
- Awesome-Federated-Learning
- FedML - The Research and Production Integrated Federated Learning Library
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- Awesome-Federated-Learning
- 2.0k
- awesome-mlops
- 5.2k
Forks
- Awesome-Federated-Learning
- 332
- awesome-mlops
- 762
Open issues
- Awesome-Federated-Learning
- 3
- awesome-mlops
- 71
Language
- Awesome-Federated-Learning
- -
- awesome-mlops
- Python
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- awesome-mlops
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- Awesome-Federated-Learning
- -
- awesome-mlops
- -
Runtime
- Awesome-Federated-Learning
- -
- awesome-mlops
- -
License
- Awesome-Federated-Learning
- -
- awesome-mlops
- -
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- awesome-mlops
- Apr 29, 2026
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- awesome-mlops
- Slowing (36%)
Days since push
- Awesome-Federated-Learning
- 1430d
- awesome-mlops
- 97d
Open issues (now)
- Awesome-Federated-Learning
- 3
- awesome-mlops
- 71
Full report
- Awesome-Federated-Learning
- Trust report
- awesome-mlops
- 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.
- Leaner open-issue backlog (3).
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.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning.
- Also covers Developer Tools, Inference & Serving.
- You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
When NOT to use awesome-mlops
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform.
- Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (chaoyanghe/Awesome-Federated-Learning) · observed Aug 4, 2026
- GitHub forks (chaoyanghe/Awesome-Federated-Learning) · observed Aug 4, 2026
- Last push (chaoyanghe/Awesome-Federated-Learning) · observed Sep 3, 2022
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Aug 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Aug 4, 2026
- Last push (kelvins/awesome-mlops) · observed Apr 29, 2026
- License file (unknown) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Federated-Learning 2.0k · awesome-mlops 5.2k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and awesome-mlops?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over awesome-mlops?
- Choose Awesome-Federated-Learning over awesome-mlops 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; Leaner open-issue backlog (3).
- When should I choose awesome-mlops over Awesome-Federated-Learning?
- Choose awesome-mlops over Awesome-Federated-Learning when Tags unique to awesome-mlops: ai, awesome, data-science, machine-learning; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving.
- 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-mlops?
- In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings.
- Is Awesome-Federated-Learning or awesome-mlops more popular on GitHub?
- awesome-mlops has more GitHub stars (5,229 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or awesome-mlops?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and awesome-mlops alternatives (Awesome-Federated-Learning markdown twin, awesome-mlops 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-mlops?
- Awesome-Federated-Learning: Dormant. awesome-mlops: 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 Awesome-Federated-Learning and awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; awesome-mlops trust report.