Comparison
Awesome-Federated-Learning vs Awesome-LLMOps
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-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Markdown twin · Awesome-Federated-Learning alternatives · Awesome-LLMOps alternatives
GraphCanon updated Oct 4, 2026
8views this month
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Trust & integrity
| Signal | Awesome-Federated-Learning | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (1491d since push) As of Oct 4, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- Awesome-Federated-Learning
- 2.0k
- Awesome-LLMOps
- 5.9k
Forks
- Awesome-Federated-Learning
- 332
- Awesome-LLMOps
- 1.1k
Open issues
- Awesome-Federated-Learning
- 3
- Awesome-LLMOps
- 317
Language
- Awesome-Federated-Learning
- -
- Awesome-LLMOps
- Shell
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- Awesome-LLMOps
- Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more.
Persona
- Awesome-Federated-Learning
- -
- Awesome-LLMOps
- -
Runtime
- Awesome-Federated-Learning
- -
- Awesome-LLMOps
- -
License
- Awesome-Federated-Learning
- -
- Awesome-LLMOps
- CC0-1.0
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- Awesome-LLMOps
- May 21, 2026
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- Awesome-Federated-Learning
- 1491d
- Awesome-LLMOps
- 121d
Open issues (now)
- Awesome-Federated-Learning
- 3
- Awesome-LLMOps
- 317
Stars delta
- Awesome-Federated-Learning
- +5 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- Awesome-Federated-Learning
- 0 (30d)
- Awesome-LLMOps
- +70 (30d)
Owner type
- Awesome-Federated-Learning
- User
- Awesome-LLMOps
- Organization
Full report
- Awesome-Federated-Learning
- Trust report
- Awesome-LLMOps
- 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-LLMOps if…
- Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio.
- - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
When NOT to use Awesome-LLMOps
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list.
- - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
Explore
Awesome-Federated-Learning trust report →Awesome-LLMOps trust report →Evaluation & Observability category →Model Training category →Computer Vision category →Data & Retrieval category →Inference & Serving category →LLM Frameworks category →Speech & Audio 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 (chaoyanghe/Awesome-Federated-Learning) · observed Oct 4, 2026
- GitHub forks (chaoyanghe/Awesome-Federated-Learning) · observed Oct 4, 2026
- Last push (chaoyanghe/Awesome-Federated-Learning) · observed Sep 3, 2022
- License file (unknown) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Federated-Learning 2.0k · Awesome-LLMOps 5.9k (synced Oct 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and Awesome-LLMOps?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over Awesome-LLMOps?
- Choose Awesome-Federated-Learning over Awesome-LLMOps 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-LLMOps over Awesome-Federated-Learning?
- Choose Awesome-LLMOps over Awesome-Federated-Learning when Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- 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-LLMOps?
- - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources.
- Is Awesome-Federated-Learning or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,941 vs 2,022). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and Awesome-LLMOps alternatives (Awesome-Federated-Learning markdown twin, Awesome-LLMOps 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-LLMOps?
- Awesome-Federated-Learning: Dormant. Awesome-LLMOps: 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-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; Awesome-LLMOps trust report.