Comparison
Awesome-Federated-Learning vs horovod
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 horovod if simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
Markdown twin · Awesome-Federated-Learning alternatives · horovod alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-Federated-Learning | horovod |
|---|---|---|
| Maintenance | Dormant (1430d since push) As of 2w · github_public_v1 | Archived (4d 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 | No lockfile (source not queried) As of 2w · deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Published findings As of 3w · openssf-scorecard@v1 |
Tagline
- Awesome-Federated-Learning
- FedML - The Research and Production Integrated Federated Learning Library
- horovod
- Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Stars
- Awesome-Federated-Learning
- 2.0k
- horovod
- 15k
Forks
- Awesome-Federated-Learning
- 332
- horovod
- 2.2k
Open issues
- Awesome-Federated-Learning
- 3
- horovod
- 406
Language
- Awesome-Federated-Learning
- -
- horovod
- Python
Adopt for
- Awesome-Federated-Learning
- FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency.
- horovod
- Simplify distributed deep learning training for TensorFlow, Keras, PyTorch, and MXNet with minimal code changes.
Persona
- Awesome-Federated-Learning
- -
- horovod
- -
Runtime
- Awesome-Federated-Learning
- -
- horovod
- -
License
- Awesome-Federated-Learning
- -
- horovod
- Other
Last pushed
- Awesome-Federated-Learning
- Sep 3, 2022
- horovod
- Jul 29, 2026
Categories
- Awesome-Federated-Learning
- Evaluation & Observability, Model Training
- horovod
- Model Training
Trust and health
Maintenance
- Awesome-Federated-Learning
- Dormant (18%)
- horovod
- Archived (8%)
Days since push
- Awesome-Federated-Learning
- 1430d
- horovod
- 4d
Archived on GitHub
- Awesome-Federated-Learning
- No
- horovod
- Yes
Open issues (now)
- Awesome-Federated-Learning
- 3
- horovod
- 406
Owner type
- Awesome-Federated-Learning
- User
- horovod
- Organization
deps.dev advisories
- Awesome-Federated-Learning
- Not queried
- horovod
- No lockfile (source not queried)
OpenSSF Scorecard
- Awesome-Federated-Learning
- Not queried
- horovod
- Published findings
Full report
- Awesome-Federated-Learning
- Trust report
- horovod
- 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 horovod if…
- Tags unique to horovod: deep-learning, distributed-training, keras, mxnet.
- When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts.
- More GitHub stars (15k vs 2.0k) - visibility, not fit.
When NOT to use horovod
- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility.
- Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
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 (horovod/horovod) · observed Aug 3, 2026
- GitHub forks (horovod/horovod) · observed Aug 3, 2026
- Last push (horovod/horovod) · observed Jul 29, 2026
- License file (Other) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-Federated-Learning 2.0k · horovod 15k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-Federated-Learning and horovod?
- Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-Federated-Learning over horovod?
- Choose Awesome-Federated-Learning over horovod 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 horovod over Awesome-Federated-Learning?
- Choose horovod over Awesome-Federated-Learning when Tags unique to horovod: deep-learning, distributed-training, keras, mxnet; When you need to scale your training across multiple GPUs or nodes with little modification to existing scripts; More GitHub stars (15k 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 horovod?
- Avoid when extensive customization beyond core training is needed, as Horovod simplifies processes which might limit flexibility. Not recommended if your project relies heavily on specific features not well-supported in Horovod's integration with frameworks like TensorFlow or PyTorch.
- Is Awesome-Federated-Learning or horovod more popular on GitHub?
- horovod has more GitHub stars (14,695 vs 2,017). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-Federated-Learning and horovod open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Awesome-Federated-Learning or horovod?
- GraphCanon lists graph-backed alternatives at Awesome-Federated-Learning alternatives and horovod alternatives (Awesome-Federated-Learning markdown twin, horovod 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 horovod?
- Awesome-Federated-Learning: Dormant. horovod: Archived. 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 horovod?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-Federated-Learning trust report; horovod trust report.