--- title: "tensorflow-federated vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-parfait-tensorflow-federated-vs-windmaple-awesome-automl" tools: ["google-parfait-tensorflow-federated", "windmaple-awesome-automl"] --- # tensorflow-federated vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick tensorflow-federated if tensorFlow Federated enables decentralized machine learning and computations without sharing raw data; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [tensorflow-federated](https://github.com/google-parfait/tensorflow-federated) reports 2.4k GitHub stars, 604 forks, and 290 open issues, last pushed Aug 3, 2026. [awesome-AutoML](https://github.com/windmaple/awesome-AutoML) has 941 stars, 156 forks, and 1 open issues, last pushed Mar 24, 2026. Figures are from public GitHub metadata via [tensorflow-federated's repository](https://github.com/google-parfait/tensorflow-federated) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | An open-source framework for machine learning and other computations on decentralized data | Curating AutoML research and resources | | Stars | 2,445 | 941 | | Forks | 604 | 156 | | Open issues | 290 | 1 | | Language | Python | - | | Adopt for | TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 133d | | Open issues (now) | 290 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/google-parfait-tensorflow-federated/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: tensorflow-federated - **Adopt for:** TensorFlow Federated enables decentralized machine learning and computations without sharing raw data. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose tensorflow-federated if… - License: tensorflow-federated is Apache-2.0, awesome-AutoML is GPL-3.0. - 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. ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, tensorflow-federated is Apache-2.0. - Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search. - When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ## 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. ## When NOT to use awesome-AutoML - If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. - When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ## Common questions ### What is the difference between tensorflow-federated and awesome-AutoML? tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose tensorflow-federated over awesome-AutoML? Choose tensorflow-federated over awesome-AutoML when License: tensorflow-federated is Apache-2.0, awesome-AutoML is GPL-3.0; 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 choose awesome-AutoML over tensorflow-federated? Choose awesome-AutoML over tensorflow-federated when License: awesome-AutoML is GPL-3.0, tensorflow-federated is Apache-2.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research. ### 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. ### When should I avoid awesome-AutoML? If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool. ### Is tensorflow-federated or awesome-AutoML more popular on GitHub? tensorflow-federated has more GitHub stars (2,445 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are tensorflow-federated and awesome-AutoML open source? Yes - both are open-source projects on GitHub (tensorflow-federated: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to tensorflow-federated or awesome-AutoML? GraphCanon lists graph-backed alternatives at [tensorflow-federated alternatives](/tools/google-parfait-tensorflow-federated/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([tensorflow-federated markdown twin](/tools/google-parfait-tensorflow-federated/alternatives.md), [awesome-AutoML markdown twin](/tools/windmaple-awesome-automl/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/google-parfait-tensorflow-federated-vs-windmaple-awesome-automl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, tensorflow-federated or awesome-AutoML? tensorflow-federated: Very active. awesome-AutoML: Slowing. 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 tensorflow-federated and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [tensorflow-federated trust report](/tools/google-parfait-tensorflow-federated/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-parfait-tensorflow-federated`](/api/graphcanon/graph?tool=google-parfait-tensorflow-federated) - 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/_