--- title: "FATE vs awesome-AutoML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/federatedai-fate-vs-windmaple-awesome-automl" tools: ["federatedai-fate", "windmaple-awesome-automl"] --- # FATE vs awesome-AutoML *GraphCanon updated Aug 4, 2026* ## Verdict Pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. [FATE](https://github.com/FederatedAI/FATE) reports 6.1k GitHub stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. [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 [FATE's repository](https://github.com/FederatedAI/FATE) and [awesome-AutoML's repository](https://github.com/windmaple/awesome-AutoML). | | [FATE](/tools/federatedai-fate.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Tagline | An Industrial Grade Federated Learning Framework | Curating AutoML research and resources | | Stars | 6,089 | 941 | | Forks | 1,568 | 156 | | Open issues | 21 | 1 | | Language | Python | - | | Adopt for | FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. | Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. | GPL-3.0 | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FATE](/tools/federatedai-fate.md) | [awesome-AutoML](/tools/windmaple-awesome-automl.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 623d | 133d | | Open issues (now) | 21 | 1 | | Owner type | Organization | User | | Full report | [trust report](/tools/federatedai-fate/trust.md) | [trust report](/tools/windmaple-awesome-automl/trust.md) | ## Decision facts: FATE - **Adopt for:** FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. - **License detail:** Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. ## Decision facts: awesome-AutoML - **Adopt for:** Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning. ## Choose when ### Choose FATE if… - License: FATE is Apache-2.0, awesome-AutoML is GPL-3.0. - Tags unique to FATE: algorithm, fate, federated-learning, machine-learning. - When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information ### Choose awesome-AutoML if… - License: awesome-AutoML is GPL-3.0, FATE 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 FATE - In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities - If your project does not require federated learning's collaborative model training across disjoint data sets ## 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 FATE and awesome-AutoML? FATE: An Industrial Grade Federated Learning Framework. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories. ### When should I choose FATE over awesome-AutoML? Choose FATE over awesome-AutoML when License: FATE is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to FATE: algorithm, fate, federated-learning, machine-learning; When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information. ### When should I choose awesome-AutoML over FATE? Choose awesome-AutoML over FATE when License: awesome-AutoML is GPL-3.0, FATE 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 FATE? In scenarios where the deployment complexity of cross-node communications is undesirable or exceeds resource capabilities If your project does not require federated learning's collaborative model training across disjoint data sets ### 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 FATE or awesome-AutoML more popular on GitHub? FATE has more GitHub stars (6,089 vs 941). Stars measure visibility, not whether either tool fits your constraints. ### Are FATE and awesome-AutoML open source? Yes - both are open-source projects on GitHub (FATE: Apache-2.0, awesome-AutoML: GPL-3.0). ### Where can I find alternatives to FATE or awesome-AutoML? GraphCanon lists graph-backed alternatives at [FATE alternatives](/tools/federatedai-fate/alternatives) and [awesome-AutoML alternatives](/tools/windmaple-awesome-automl/alternatives) ([FATE markdown twin](/tools/federatedai-fate/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/federatedai-fate-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, FATE or awesome-AutoML? FATE: Dormant. 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 FATE and awesome-AutoML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FATE trust report](/tools/federatedai-fate/trust); [awesome-AutoML trust report](/tools/windmaple-awesome-automl/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=federatedai-fate`](/api/graphcanon/graph?tool=federatedai-fate) - 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/_