Comparison
Awesome-Federated-Learning vs LibFewShot
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 LibFewShot if libFewShot is a focused library designed specifically for few-shot learning tasks, emphasizing both fine-tuning and meta-learning techniques. It is particularly optimized for use cases involving image classification.
Markdown twin · Awesome-Federated-Learning alternatives · LibFewShot alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | LibFewShot |
|---|---|---|
| Maintenance | Dormant (1430d since push) As of 2w · github_public_v1 | Slowing (300d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of today · 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
- LibFewShot
- LibFewShot: A Comprehensive Library for Few-shot Learning
Stars
- Awesome-Federated-Learning
- 2.0k
- LibFewShot
- 1.1k
Forks
- Awesome-Federated-Learning
- 332
- LibFewShot
- 200
Open issues
- Awesome-Federated-Learning
- 3
- LibFewShot
- 10
Language
- Awesome-Federated-Learning
- -
- LibFewShot
- Python
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- LibFewShot
- LibFewShot is a focused library designed specifically for few-shot learning tasks, emphasizing both fine-tuning and meta-learning techniques. It is particularly optimized for use cases involving image classification.
Persona
- Awesome-Federated-Learning
- -
- LibFewShot
- -
Runtime
- Awesome-Federated-Learning
- -
- LibFewShot
- -
License
- Awesome-Federated-Learning
- -
- LibFewShot
- MIT
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- LibFewShot
- Oct 27, 2025
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- LibFewShot
- Computer Vision, Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- LibFewShot
- Slowing (36%)
Days since push
- Awesome-Federated-Learning
- 1430d
- LibFewShot
- 300d
Open issues (now)
- Awesome-Federated-Learning
- 3
- LibFewShot
- 10
Stars delta
- Awesome-Federated-Learning
- Unknown
- LibFewShot
- -2 (30d)
Open issues delta
- Awesome-Federated-Learning
- Unknown
- LibFewShot
- 0 (30d)
Owner type
- Awesome-Federated-Learning
- User
- LibFewShot
- Organization
Full report
- Awesome-Federated-Learning
- Trust report
- LibFewShot
- Trust report
Choose Awesome-Federated-Learning if…
- Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision.
- Also covers Evaluation & Observability.
- 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.
Choose LibFewShot if…
- Pricing: LibFewShot is open-source under the MIT license, making it freely available and modifiable. However, advanced features or support might require contributions or additional resources..
- Tags unique to LibFewShot: few-shot-learning, fine-tuning, image-classification, meta-learning.
- Also covers Computer Vision.
- When your project involves few-shot learning scenarios where adapting models with limited labeled data for image classification tasks is critical.
When NOT to use LibFewShot
- Last GitHub push was 301 days ago (slowing maintenance, Oct 27, 2025). Validate activity before betting a new project on LibFewShot.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
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 (RL-VIG/LibFewShot) · observed Aug 24, 2026
- GitHub forks (RL-VIG/LibFewShot) · observed Aug 24, 2026
- Last push (RL-VIG/LibFewShot) · observed Oct 27, 2025
- License file (MIT) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Federated-Learning 2.0k · LibFewShot 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and LibFewShot?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. LibFewShot: LibFewShot: A Comprehensive Library for Few-shot Learning. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over LibFewShot?
- Choose Awesome-Federated-Learning over LibFewShot when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, computer-vision; Also covers Evaluation & Observability; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches.
- When should I choose LibFewShot over Awesome-Federated-Learning?
- Choose LibFewShot over Awesome-Federated-Learning when Pricing: LibFewShot is open-source under the MIT license, making it freely available and modifiable. However, advanced features or support might require contributions or additional resources.; Tags unique to LibFewShot: few-shot-learning, fine-tuning, image-classification, meta-learning; Also covers Computer Vision; When your project involves few-shot learning scenarios where adapting models with limited labeled data for image classification tasks is critical.
- 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 LibFewShot?
- Last GitHub push was 301 days ago (slowing maintenance, Oct 27, 2025). Validate activity before betting a new project on LibFewShot. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is Awesome-Federated-Learning or LibFewShot more popular on GitHub?
- Awesome-Federated-Learning has more GitHub stars (2,017 vs 1,069). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and LibFewShot open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or LibFewShot?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and LibFewShot alternatives (Awesome-Federated-Learning markdown twin, LibFewShot 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 LibFewShot?
- Awesome-Federated-Learning: Dormant. LibFewShot: 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 LibFewShot?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; LibFewShot trust report.