--- title: "Awesome-Federated-Learning vs mmengine" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-open-mmlab-mmengine" tools: ["chaoyanghe-awesome-federated-learning", "open-mmlab-mmengine"] --- # Awesome-Federated-Learning vs mmengine *GraphCanon updated Aug 4, 2026* ## 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 mmengine if mMEngine, part of OpenMMLab, serves as a foundational library for training deep learning models with PyTorch in Python. [Awesome-Federated-Learning](https://github.com/chaoyanghe/Awesome-Federated-Learning) reports 2.0k GitHub stars, 332 forks, and 3 open issues, last pushed Sep 3, 2022. [mmengine](https://mmengine.readthedocs.io/) has 1.5k stars, 455 forks, and 260 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [mmengine's repository](https://github.com/open-mmlab/mmengine). | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [mmengine](/tools/open-mmlab-mmengine.md) | | --- | --- | --- | | Tagline | FedML - The Research and Production Integrated Federated Learning Library | OpenMMLab Foundational Library for Training Deep Learning Models | | Stars | 2,017 | 1,482 | | Forks | 332 | 455 | | Open issues | 3 | 260 | | Language | - | Python | | Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | MMEngine, part of OpenMMLab, serves as a foundational library for training deep learning models with PyTorch in Python. | | Persona | - | - | | Runtime | - | - | | License | - | MMEngine is distributed under the Apache 2.0 License. | | Categories | Evaluation & Observability, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [mmengine](/tools/open-mmlab-mmengine.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1430d | 18d | | Open issues (now) | 3 | 260 | | Owner type | User | Organization | | Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/open-mmlab-mmengine/trust.md) | ## Decision facts: Awesome-Federated-Learning - **Adopt for:** FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. ## Decision facts: mmengine - **Pricing:** freemium - The core functionality for model training offered through MMEngine is accessible without cost due to its licensing terms (Apache 2.0). - **Adopt for:** MMEngine, part of OpenMMLab, serves as a foundational library for training deep learning models with PyTorch in Python. - **License detail:** MMEngine is distributed under the Apache 2.0 License. ## Choose when ### Choose Awesome-Federated-Learning if… - Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning. - Also covers Evaluation & Observability. - When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### Choose mmengine if… - Pricing: The core functionality for model training offered through MMEngine is accessible without cost due to its licensing terms (Apache 2.0).. - Tags unique to mmengine: ai, deep-learning, machine-learning, python. - - Use MMEngine when you are leveraging PyTorch and require a solid foundation for your deep learning model training processes. ## 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. ## When NOT to use mmengine - - Avoid using MMEngine if your project requires a Python version outside of the supported range (e.g., Python 3.12+). - - If you are working with frameworks other than PyTorch, MMEngine might not be suitable as it is specifically optimized for PyTorch support. - - Consider an alternative if you are looking for more flexibility beyond the specific use cases catered to by OpenMMLab and do not want to be tied into their ecosystem. ## Common questions ### What is the difference between Awesome-Federated-Learning and mmengine? Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. mmengine: OpenMMLab Foundational Library for Training Deep Learning Models. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Federated-Learning over mmengine? Choose Awesome-Federated-Learning over mmengine when Tags unique to Awesome-Federated-Learning: adversarial-attack-and-defense, communication-efficiency, computation-efficiency, continual-learning; Also covers Evaluation & Observability; When developing federated learning solutions that require comprehensive features like hierarchical models and decentralized approaches. ### When should I choose mmengine over Awesome-Federated-Learning? Choose mmengine over Awesome-Federated-Learning when Pricing: The core functionality for model training offered through MMEngine is accessible without cost due to its licensing terms (Apache 2.0).; Tags unique to mmengine: ai, deep-learning, machine-learning, python; - Use MMEngine when you are leveraging PyTorch and require a solid foundation for your deep learning model training processes. ### 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 mmengine? - Avoid using MMEngine if your project requires a Python version outside of the supported range (e.g., Python 3.12+). - If you are working with frameworks other than PyTorch, MMEngine might not be suitable as it is specifically optimized for PyTorch support. - Consider an alternative if you are looking for more flexibility beyond the specific use cases catered to by OpenMMLab and do not want to be tied into their ecosystem. ### Is Awesome-Federated-Learning or mmengine more popular on GitHub? Awesome-Federated-Learning has more GitHub stars (2,017 vs 1,482). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Federated-Learning and mmengine open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Federated-Learning or mmengine? GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [mmengine alternatives](/tools/open-mmlab-mmengine/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [mmengine markdown twin](/tools/open-mmlab-mmengine/alternatives.md)), 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](/compare/chaoyanghe-awesome-federated-learning-vs-open-mmlab-mmengine.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-Federated-Learning or mmengine? Awesome-Federated-Learning: Dormant. mmengine: 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 mmengine? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [mmengine trust report](/tools/open-mmlab-mmengine/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning`](/api/graphcanon/graph?tool=chaoyanghe-awesome-federated-learning) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_