--- title: "Awesome-Federated-Learning vs mesh" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/chaoyanghe-awesome-federated-learning-vs-tensorflow-mesh" tools: ["chaoyanghe-awesome-federated-learning", "tensorflow-mesh"] --- # Awesome-Federated-Learning vs mesh *GraphCanon updated Aug 7, 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 mesh if mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license. [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. [mesh](https://github.com/tensorflow/mesh) has 1.6k stars, 255 forks, and 98 open issues, last pushed Nov 17, 2023. Figures are from public GitHub metadata via [Awesome-Federated-Learning's repository](https://github.com/chaoyanghe/Awesome-Federated-Learning) and [mesh's repository](https://github.com/tensorflow/mesh). | | [Awesome-Federated-Learning](/tools/chaoyanghe-awesome-federated-learning.md) | [mesh](/tools/tensorflow-mesh.md) | | --- | --- | --- | | Tagline | FedML - The Research and Production Integrated Federated Learning Library | Mesh TensorFlow: Model Parallelism Made Easier | | Stars | 2,017 | 1,630 | | Forks | 332 | 255 | | Open issues | 3 | 98 | | Language | - | Python | | Adopt for | FedML library for federated learning with emphasis on research and production, featuring adversarial attack defenses and resource efficiency. | Mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | 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) | [mesh](/tools/tensorflow-mesh.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Archived (8%) | | Days since push | 1430d | 993d | | Archived on GitHub | No | Yes | | Open issues (now) | 3 | 98 | | Owner type | User | Organization | | Full report | [trust report](/tools/chaoyanghe-awesome-federated-learning/trust.md) | [trust report](/tools/tensorflow-mesh/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: mesh - **Adopt for:** Mesh TensorFlow supports simplified model parallelism across multiple devices in Python under the Apache-2.0 license. - **License detail:** Apache-2.0 ## Choose when ### 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. ### Choose mesh if… - Tags unique to mesh: model parallelism, python, tensorflow. - When working on large models that benefit from being split across many devices. - More recently updated (last pushed Nov 17, 2023). ## 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 mesh - If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation. - For projects with limited GPU/TPU resources where multi-device parallelism is not required. ## Common questions ### What is the difference between Awesome-Federated-Learning and mesh? Awesome-Federated-Learning: FedML - The Research and Production Integrated Federated Learning Library. mesh: Mesh TensorFlow: Model Parallelism Made Easier. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-Federated-Learning over mesh? Choose Awesome-Federated-Learning over mesh 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 mesh over Awesome-Federated-Learning? Choose mesh over Awesome-Federated-Learning when Tags unique to mesh: model parallelism, python, tensorflow; When working on large models that benefit from being split across many devices; More recently updated (last pushed Nov 17, 2023). ### 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 mesh? If you are looking for a tool that simplifies other aspects of machine learning beyond model-parallel computation. For projects with limited GPU/TPU resources where multi-device parallelism is not required. ### Is Awesome-Federated-Learning or mesh more popular on GitHub? Awesome-Federated-Learning has more GitHub stars (2,017 vs 1,630). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-Federated-Learning and mesh open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to Awesome-Federated-Learning or mesh? GraphCanon lists graph-backed alternatives at [Awesome-Federated-Learning alternatives](/tools/chaoyanghe-awesome-federated-learning/alternatives) and [mesh alternatives](/tools/tensorflow-mesh/alternatives) ([Awesome-Federated-Learning markdown twin](/tools/chaoyanghe-awesome-federated-learning/alternatives.md), [mesh markdown twin](/tools/tensorflow-mesh/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-tensorflow-mesh.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 mesh? Awesome-Federated-Learning: Dormant. mesh: 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 mesh? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-Federated-Learning trust report](/tools/chaoyanghe-awesome-federated-learning/trust); [mesh trust report](/tools/tensorflow-mesh/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/_