Comparison
awesome-federated-learning vs RLTF
Verdict
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; pick RLTF if rLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Markdown twin · awesome-federated-learning alternatives · RLTF alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | awesome-federated-learning | RLTF |
|---|---|---|
| Maintenance | Slowing (261d since push) As of 2w · github_public_v1 | Dormant (669d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal 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 | Published findings 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
- Curated federated learning resources including papers, blogs, videos, and projects
- RLTF
- Accepted by Transactions on Machine Learning Research (TMLR)
Stars
- awesome-federated-learning
- 738
- RLTF
- 134
Forks
- awesome-federated-learning
- 98
- RLTF
- 7
Open issues
- awesome-federated-learning
- 0
- RLTF
- 0
Language
- awesome-federated-learning
- Shell
- RLTF
- Python
Adopt for
- awesome-federated-learning
- awesome-federated-learning is a curated collection of federated learning resources with a focus on communication efficiency and privacy preservation.
- RLTF
- RLTF implements reinforcement learning for text generation using elements from CodeRL, APPS, and transformers.
Persona
- awesome-federated-learning
- -
- RLTF
- -
Runtime
- awesome-federated-learning
- -
- RLTF
- -
License
- awesome-federated-learning
- MIT
- RLTF
- BSD-3-Clause
Last pushed
- awesome-federated-learning
- Nov 16, 2025
- RLTF
- Oct 5, 2024
Categories
- awesome-federated-learning
- Model Training
- RLTF
- Model Training
Trust and health
Maintenance
- awesome-federated-learning
- Slowing (36%)
- RLTF
- Dormant (18%)
Days since push
- awesome-federated-learning
- 261d
- RLTF
- 669d
OSV dependency advisories
- awesome-federated-learning
- No lockfile (source not queried)
- RLTF
- Published findings
Full report
- awesome-federated-learning
- Trust report
- RLTF
- Trust report
Choose awesome-federated-learning if…
- awesome-federated-learning is primarily Shell; RLTF is Python.
- License: awesome-federated-learning is MIT, RLTF is BSD-3-Clause.
- Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, federated-learning, machine-learning.
- 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
Choose RLTF if…
- RLTF is primarily Python; awesome-federated-learning is Shell.
- License: RLTF is BSD-3-Clause, awesome-federated-learning is MIT.
- Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions.
- Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
When NOT to use RLTF
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation.
- Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (Zyq-scut/RLTF) · observed Aug 5, 2026
- GitHub forks (Zyq-scut/RLTF) · observed Aug 5, 2026
- Last push (Zyq-scut/RLTF) · observed Oct 5, 2024
- License file (BSD-3-Clause) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-federated-learning 738 · RLTF 134 (synced Aug 4, 2026).
Common questions
- What is the difference between awesome-federated-learning and RLTF?
- awesome-federated-learning: Curated federated learning resources including papers, blogs, videos, and projects. RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories.
- When should I choose awesome-federated-learning over RLTF?
- Choose awesome-federated-learning over RLTF when awesome-federated-learning is primarily Shell; RLTF is Python; License: awesome-federated-learning is MIT, RLTF is BSD-3-Clause; Tags unique to awesome-federated-learning: communication-efficiency, data-privacy, federated-learning, machine-learning; Use it if you need organized materials for research and projects in areas like statistical heterogeneity or decentralized FL.
- When should I choose RLTF over awesome-federated-learning?
- Choose RLTF over awesome-federated-learning when RLTF is primarily Python; awesome-federated-learning is Shell; License: RLTF is BSD-3-Clause, awesome-federated-learning is MIT; Tags unique to RLTF: apps, bsd-license, code-rl, open-source-contributions; Use RLTF when you need advanced Reinforcement Learning models specifically tuned for text generation tasks.
- 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
- When should I avoid RLTF?
- Avoid RLTF if your project does not require reinforcement learning techniques, especially focused on text generation. Do not use this tool if your work is incompatible with components from CodeRL, APPS, or transformers.
- Is awesome-federated-learning or RLTF more popular on GitHub?
- awesome-federated-learning has more GitHub stars (738 vs 134). Stars measure visibility, not whether either tool fits your constraints.
- Are awesome-federated-learning and RLTF open source?
- Yes - both are open-source projects on GitHub (awesome-federated-learning: MIT, RLTF: BSD-3-Clause).
- Where can I find alternatives to awesome-federated-learning or RLTF?
- GraphCanon lists graph-backed alternatives at awesome-federated-learning alternatives and RLTF alternatives (awesome-federated-learning markdown twin, RLTF 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 RLTF?
- awesome-federated-learning: Slowing. RLTF: Dormant. 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 RLTF?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: awesome-federated-learning trust report; RLTF trust report.