Comparison
Awesome-Federated-Learning vs flower
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 flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.
Markdown twin · Awesome-Federated-Learning alternatives · flower alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | flower |
|---|---|---|
| Maintenance | Dormant (1430d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization 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
- flower
- A Friendly Federated AI Framework
Stars
- Awesome-Federated-Learning
- 2.0k
- flower
- 7.1k
Forks
- Awesome-Federated-Learning
- 332
- flower
- 1.2k
Open issues
- Awesome-Federated-Learning
- 3
- flower
- 368
Language
- Awesome-Federated-Learning
- -
- flower
- Python
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- flower
- A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.
Persona
- Awesome-Federated-Learning
- -
- flower
- -
Runtime
- Awesome-Federated-Learning
- -
- flower
- -
License
- Awesome-Federated-Learning
- -
- flower
- Apache-2.0
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- flower
- Aug 4, 2026
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- flower
- Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- flower
- Very active (96%)
Days since push
- Awesome-Federated-Learning
- 1430d
- flower
- 0d
Open issues (now)
- Awesome-Federated-Learning
- 3
- flower
- 368
Owner type
- Awesome-Federated-Learning
- User
- flower
- Organization
Full report
- Awesome-Federated-Learning
- Trust report
- flower
- 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 flower if…
- Tags unique to flower: ai-frameworks, python, pytorch, tensorflow.
- When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning
- More GitHub stars (7.1k vs 2.0k) - visibility, not fit.
When NOT to use flower
- Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support
- Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer
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 (flwrlabs/flower) · observed Aug 4, 2026
- GitHub forks (flwrlabs/flower) · observed Aug 4, 2026
- Last push (flwrlabs/flower) · observed Aug 4, 2026
- License file (Apache-2.0) · 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 · flower 7.1k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and flower?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. flower: A Friendly Federated AI Framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over flower?
- Choose Awesome-Federated-Learning over flower 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 flower over Awesome-Federated-Learning?
- Choose flower over Awesome-Federated-Learning when Tags unique to flower: ai-frameworks, python, pytorch, tensorflow; When you require support for a wide range of machine learning frameworks including PyTorch, TensorFlow, and scikit-learn to integrate federated learning; More GitHub stars (7.1k 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 flower?
- Avoid if your use case demands real-time model updates or integration with specific ML frameworks not covered by Flower's framework support Not recommended for projects where the federated learning setup requires extensive customization beyond what the extendable components offer
- Is Awesome-Federated-Learning or flower more popular on GitHub?
- flower has more GitHub stars (7,067 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and flower open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or flower?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and flower alternatives (Awesome-Federated-Learning markdown twin, flower 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 flower?
- Awesome-Federated-Learning: Dormant. flower: Very active. 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 flower?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; flower trust report.