Comparison
flower vs awesome-federated-learning
Verdict
Pick flower if a customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python; pick awesome-federated-learning if awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
Markdown twin · flower alternatives · awesome-federated-learning alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | flower | awesome-federated-learning |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Slowing (261d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization 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
- flower
- A Friendly Federated AI Framework
- awesome-federated-learning
- Curated federated learning resources including papers, blogs, videos, and projects
Stars
- flower
- 7.1k
- awesome-federated-learning
- 738
Forks
- flower
- 1.2k
- awesome-federated-learning
- 98
Open issues
- flower
- 368
- awesome-federated-learning
- 0
Language
- flower
- Python
- awesome-federated-learning
- Shell
Adopt for
- flower
- A customizable, extendable federated learning framework supporting multiple ML frameworks, coded in Python.
- awesome-federated-learning
- awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
Persona
- flower
- -
- awesome-federated-learning
- -
Runtime
- flower
- -
- awesome-federated-learning
- -
License
- flower
- Apache-2.0
- awesome-federated-learning
- MIT
Last pushed
- flower
- Aug 4, 2026
- awesome-federated-learning
- Nov 16, 2025
Categories
- flower
- Model Training
- awesome-federated-learning
- Model Training
Trust and health
Maintenance
- flower
- Very active (96%)
- awesome-federated-learning
- Slowing (36%)
Days since push
- flower
- 0d
- awesome-federated-learning
- 261d
Open issues (now)
- flower
- 368
- awesome-federated-learning
- 0
Owner type
- flower
- Organization
- awesome-federated-learning
- User
Full report
- flower
- Trust report
- awesome-federated-learning
- Trust report
Choose flower if…
- flower is primarily Python; awesome-federated-learning is Shell.
- License: flower is Apache-2.0, awesome-federated-learning is MIT.
- 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
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
Choose awesome-federated-learning if…
- awesome-federated-learning is primarily Shell; flower is Python.
- License: awesome-federated-learning is MIT, flower is Apache-2.0.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid.
- Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL
When NOT to use awesome-federated-learning
- Avoid if your project does not require federated learning-specific optimizations or frameworks
- Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (weimingwill/awesome-federated-learning) · observed Aug 4, 2026
- GitHub forks (weimingwill/awesome-federated-learning) · observed Aug 4, 2026
- Last push (weimingwill/awesome-federated-learning) · observed Nov 16, 2025
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: flower 7.1k · awesome-federated-learning 738 (synced Aug 4, 2026).
Common questions
- What is the difference between flower and awesome-federated-learning?
- flower: A Friendly Federated AI Framework. awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. See the comparison table for live GitHub stats and shared categories.
- When should I choose flower over awesome-federated-learning?
- Choose flower over awesome-federated-learning when flower is primarily Python; awesome-federated-learning is Shell; License: flower is Apache-2.0, awesome-federated-learning is MIT; 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.
- When should I choose awesome-federated-learning over flower?
- Choose awesome-federated-learning over flower when awesome-federated-learning is primarily Shell; flower is Python; License: awesome-federated-learning is MIT, flower is Apache-2.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, machine-learning, non-iid; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.
- 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
- When should I avoid awesome-federated-learning?
- Avoid if your project does not require federated learning-specific optimizations or frameworks Not suitable if you only need general machine learning resources without focus on privacy and efficiency in FL
- Is flower or awesome-federated-learning more popular on GitHub?
- flower has more GitHub stars (7,067 vs 738). Stars measure visibility, not whether either tool fits your constraints.
- Are flower and awesome-federated-learning open source?
- Yes - both are open-source projects on GitHub (flower: Apache-2.0, awesome-federated-learning: MIT).
- Where can I find alternatives to flower or awesome-federated-learning?
- GraphCanon lists graph-backed alternatives at flower alternatives and awesome-federated-learning alternatives (flower markdown twin, awesome-federated-learning 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, flower or awesome-federated-learning?
- flower: Very active. awesome-federated-learning: 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 flower and awesome-federated-learning?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: flower trust report; awesome-federated-learning trust report.