Comparison
Awesome-Federated-Learning vs FLAML
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 FLAML if fLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting.
Markdown twin · Awesome-Federated-Learning alternatives · FLAML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | FLAML |
|---|---|---|
| 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
- FLAML
- A fast library for AutoML and tuning
Stars
- Awesome-Federated-Learning
- 2.0k
- FLAML
- 4.4k
Forks
- Awesome-Federated-Learning
- 332
- FLAML
- 559
Open issues
- Awesome-Federated-Learning
- 3
- FLAML
- 180
Language
- Awesome-Federated-Learning
- -
- FLAML
- Jupyter Notebook
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- FLAML
- FLAML streamlines AutoML and tuning tasks with optimized algorithms for model selection and hyperparameter optimization across classification, regression, NLP, and time-series forecasting.
Persona
- Awesome-Federated-Learning
- -
- FLAML
- -
Runtime
- Awesome-Federated-Learning
- -
- FLAML
- -
License
- Awesome-Federated-Learning
- -
- FLAML
- MIT
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- FLAML
- Aug 3, 2026
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- FLAML
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- FLAML
- Very active (96%)
Days since push
- Awesome-Federated-Learning
- 1430d
- FLAML
- 0d
Open issues (now)
- Awesome-Federated-Learning
- 3
- FLAML
- 180
Owner type
- Awesome-Federated-Learning
- User
- FLAML
- Organization
Full report
- Awesome-Federated-Learning
- Trust report
- FLAML
- 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 FLAML if…
- Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning.
- FLAML ships Docker support for self-hosted deployment.
- When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML.
When NOT to use FLAML
- When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available.
- If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting.
- For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations.
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 (microsoft/FLAML) · observed Aug 4, 2026
- GitHub forks (microsoft/FLAML) · observed Aug 4, 2026
- Last push (microsoft/FLAML) · observed Aug 3, 2026
- 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: Awesome-Federated-Learning 2.0k · FLAML 4.4k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and FLAML?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. FLAML: A fast library for AutoML and tuning. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over FLAML?
- Choose Awesome-Federated-Learning over FLAML 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 FLAML over Awesome-Federated-Learning?
- Choose FLAML over Awesome-Federated-Learning when Tags unique to FLAML: automated-machine-learning, classification, data-science, deep-learning; FLAML ships Docker support for self-hosted deployment; When working with Python >= 3.10 and < 3.14 to ensure full support of all models in FLAML.
- 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 FLAML?
- When your project is restricted to using Python versions below 3.10 or equal to and above 3.14, as FLAML's full feature support may not be available. If the scope of work does not include AutoML tasks such as hyperparameter optimization or model selection for classification, regression, NLP, or time-series forecasting. For users who need cross-language compatibility beyond Python and Jupyter Notebook environments; FLAML primarily supports Python-based operations.
- Is Awesome-Federated-Learning or FLAML more popular on GitHub?
- FLAML has more GitHub stars (4,385 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and FLAML open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or FLAML?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and FLAML alternatives (Awesome-Federated-Learning markdown twin, FLAML 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 FLAML?
- Awesome-Federated-Learning: Dormant. FLAML: 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 FLAML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; FLAML trust report.