--- title: "Awesome-AutoDL vs tensorflow-federated" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-google-parfait-tensorflow-federated" tools: ["d-x-y-awesome-autodl", "google-parfait-tensorflow-federated"] --- # Awesome-AutoDL vs tensorflow-federated *GraphCanon updated Oct 4, 2026* ## Verdict Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick tensorflow-federated if tensorFlow Federated enables decentralized machine learning and computations without sharing raw data. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 317 forks, and 2 open issues, last pushed Sep 26, 2022. [tensorflow-federated](https://github.com/google-parfait/tensorflow-federated) has 2.5k stars, 605 forks, and 292 open issues, last pushed Oct 1, 2026. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [tensorflow-federated's repository](https://github.com/google-parfait/tensorflow-federated). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | An open-source framework for machine learning and other computations on decentralized data | | Stars | 2,344 | 2,454 | | Forks | 317 | 605 | | Open issues | 2 | 292 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | Apache-2.0 | | Categories | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1469d | 1d | | Open issues (now) | 2 | 292 | | Stars delta | +5 (30d) | +9 (30d) | | Open issues delta | 0 (30d) | +2 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/google-parfait-tensorflow-federated/trust.md) | ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Decision facts: tensorflow-federated - **Adopt for:** TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, tensorflow-federated is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - Also covers Developer Tools. - Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### Choose tensorflow-federated if… - License: tensorflow-federated is Apache-2.0, Awesome-AutoDL is MIT. - Tags unique to tensorflow-federated: decentralized data, federated-learning, tensorflow. - If you need to develop federated learning algorithms that can train models across multiple devices or servers while keeping the training data distributed and secure. ## When NOT to use Awesome-AutoDL - Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. - Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ## When NOT to use tensorflow-federated - Avoid if you require centralized data for your learning models, as TensorFlow Federated's strength lies in its capabilities to maintain decentralized datasets. - If real-time computation or very low latency requirements are critical to your project; the nature of federated learning involves significant overhead and does not perform well in such scenarios. ## Common questions ### What is the difference between Awesome-AutoDL and tensorflow-federated? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over tensorflow-federated? Choose Awesome-AutoDL over tensorflow-federated when License: Awesome-AutoDL is MIT, tensorflow-federated is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS). ### When should I choose tensorflow-federated over Awesome-AutoDL? Choose tensorflow-federated over Awesome-AutoDL when License: tensorflow-federated is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to tensorflow-federated: decentralized data, federated-learning, tensorflow; If you need to develop federated learning algorithms that can train models across multiple devices or servers while keeping the training data distributed and secure. ### When should I avoid Awesome-AutoDL? Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide. ### When should I avoid tensorflow-federated? Avoid if you require centralized data for your learning models, as TensorFlow Federated's strength lies in its capabilities to maintain decentralized datasets. If real-time computation or very low latency requirements are critical to your project; the nature of federated learning involves significant overhead and does not perform well in such scenarios. ### Is Awesome-AutoDL or tensorflow-federated more popular on GitHub? tensorflow-federated has more GitHub stars (2,454 vs 2,344). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and tensorflow-federated open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, tensorflow-federated: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or tensorflow-federated? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [tensorflow-federated alternatives](/tools/google-parfait-tensorflow-federated/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [tensorflow-federated markdown twin](/tools/google-parfait-tensorflow-federated/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/d-x-y-awesome-autodl-vs-google-parfait-tensorflow-federated.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or tensorflow-federated? Awesome-AutoDL: Dormant. tensorflow-federated: Very 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-AutoDL and tensorflow-federated? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [tensorflow-federated trust report](/tools/google-parfait-tensorflow-federated/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - 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/_