Comparison
Awesome-Federated-Learning vs mesh
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 mesh if mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license.
Markdown twin · Awesome-Federated-Learning alternatives · mesh alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | mesh |
|---|---|---|
| Maintenance | Dormant (1430d since push) As of 2w · github_public_v1 | Archived (993d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 1w · 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
- mesh
- Mesh TensorFlow: Model Parallelism Made Easier
Stars
- Awesome-Federated-Learning
- 2.0k
- mesh
- 1.6k
Forks
- Awesome-Federated-Learning
- 332
- mesh
- 255
Open issues
- Awesome-Federated-Learning
- 3
- mesh
- 98
Language
- Awesome-Federated-Learning
- -
- mesh
- Python
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- mesh
- Mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license.
Persona
- Awesome-Federated-Learning
- -
- mesh
- -
Runtime
- Awesome-Federated-Learning
- -
- mesh
- -
License
- Awesome-Federated-Learning
- -
- mesh
- Apache-2.0
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- mesh
- Nov 17, 2023
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- mesh
- Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- mesh
- Archived (8%)
Days since push
- Awesome-Federated-Learning
- 1430d
- mesh
- 993d
Archived on GitHub
- Awesome-Federated-Learning
- No
- mesh
- Yes
Open issues (now)
- Awesome-Federated-Learning
- 3
- mesh
- 98
Owner type
- Awesome-Federated-Learning
- User
- mesh
- Organization
Full report
- Awesome-Federated-Learning
- Trust report
- mesh
- 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 mesh if…
- Tags unique to mesh: model parallelism, python, tensorflow.
- When working on large models that benefit from being split across many devices.
- More recently updated (last pushed Nov 17, 2023).
When NOT to use mesh
- If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation.
- For projects with limited GPU/TPU resources where multi-device parallelism is not required.
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 (tensorflow/mesh) · observed Aug 7, 2026
- GitHub forks (tensorflow/mesh) · observed Aug 7, 2026
- Last push (tensorflow/mesh) · observed Nov 17, 2023
- License file (Apache-2.0) · observed Aug 7, 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 · mesh 1.6k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and mesh?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. mesh: Mesh TensorFlow: Model Parallelism Made Easier. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over mesh?
- Choose Awesome-Federated-Learning over mesh 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 mesh over Awesome-Federated-Learning?
- Choose mesh over Awesome-Federated-Learning when Tags unique to mesh: model parallelism, python, tensorflow; When working on large models that benefit from being split across many devices; More recently updated (last pushed Nov 17, 2023).
- 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 mesh?
- If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation. For projects with limited GPU/TPU resources where multi-device parallelism is not required.
- Is Awesome-Federated-Learning or mesh more popular on GitHub?
- Awesome-Federated-Learning has more GitHub stars (2,017 vs 1,630). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and mesh open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or mesh?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and mesh alternatives (Awesome-Federated-Learning markdown twin, mesh 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 mesh?
- Awesome-Federated-Learning: Dormant. mesh: Archived. 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 mesh?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; mesh trust report.