Comparison
FATE vs awesome-federated-learning
Verdict
Pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes; 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 · FATE alternatives · awesome-federated-learning alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | FATE | awesome-federated-learning |
|---|---|---|
| Maintenance | Dormant (623d 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
- FATE
- An Industrial Grade Federated Learning Framework
- awesome-federated-learning
- Curated federated learning resources including papers, blogs, videos, and projects
Stars
- FATE
- 6.1k
- awesome-federated-learning
- 738
Forks
- FATE
- 1.6k
- awesome-federated-learning
- 98
Open issues
- FATE
- 21
- awesome-federated-learning
- 0
Language
- FATE
- Python
- awesome-federated-learning
- Shell
Adopt for
- FATE
- FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.
- awesome-federated-learning
- awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
Persona
- FATE
- -
- awesome-federated-learning
- -
Runtime
- FATE
- -
- awesome-federated-learning
- -
License
- FATE
- Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users.
- awesome-federated-learning
- MIT
Last pushed
- FATE
- Nov 19, 2024
- awesome-federated-learning
- Nov 16, 2025
Categories
- FATE
- Model Training
- awesome-federated-learning
- Model Training
Trust and health
Maintenance
- FATE
- Dormant (18%)
- awesome-federated-learning
- Slowing (36%)
Days since push
- FATE
- 623d
- awesome-federated-learning
- 261d
Open issues (now)
- FATE
- 21
- awesome-federated-learning
- 0
Owner type
- FATE
- Organization
- awesome-federated-learning
- User
Full report
- FATE
- Trust report
- awesome-federated-learning
- Trust report
Choose FATE if…
- FATE is primarily Python; awesome-federated-learning is Shell.
- License: FATE is Apache-2.0, awesome-federated-learning is MIT.
- Tags unique to FATE: algorithm, fate, privacy-preserving.
- When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information
When NOT to use FATE
- In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities
- If your project does not require federated learning's collaborative model training across disjoint data sets
Choose awesome-federated-learning if…
- awesome-federated-learning is primarily Shell; FATE is Python.
- License: awesome-federated-learning is MIT, FATE is Apache-2.0.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, non-iid, statistical-heterogeneity.
- 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 (FederatedAI/FATE) · observed Aug 4, 2026
- GitHub forks (FederatedAI/FATE) · observed Aug 4, 2026
- Last push (FederatedAI/FATE) · observed Nov 19, 2024
- 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: FATE 6.1k · awesome-federated-learning 738 (synced Aug 4, 2026).
Common questions
- What is the difference between FATE and awesome-federated-learning?
- FATE: An Industrial Grade Federated Learning 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 FATE over awesome-federated-learning?
- Choose FATE over awesome-federated-learning when FATE is primarily Python; awesome-federated-learning is Shell; License: FATE is Apache-2.0, awesome-federated-learning is MIT; Tags unique to FATE: algorithm, fate, privacy-preserving; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information.
- When should I choose awesome-federated-learning over FATE?
- Choose awesome-federated-learning over FATE when awesome-federated-learning is primarily Shell; FATE is Python; License: awesome-federated-learning is MIT, FATE is Apache-2.0; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, non-iid, statistical-heterogeneity; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.
- When should I avoid FATE?
- In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities If your project does not require federated learning's collaborative model training across disjoint data sets
- 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 FATE or awesome-federated-learning more popular on GitHub?
- FATE has more GitHub stars (6,089 vs 738). Stars measure visibility, not whether either tool fits your constraints.
- Are FATE and awesome-federated-learning open source?
- Yes - both are open-source projects on GitHub (FATE: Apache-2.0, awesome-federated-learning: MIT).
- Where can I find alternatives to FATE or awesome-federated-learning?
- GraphCanon lists graph-backed alternatives at FATE alternatives and awesome-federated-learning alternatives (FATE 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, FATE or awesome-federated-learning?
- FATE: Dormant. 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 FATE and awesome-federated-learning?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: FATE trust report; awesome-federated-learning trust report.