--- title: "FEDOT vs FATE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aimclub-fedot-vs-federatedai-fate" tools: ["aimclub-fedot", "federatedai-fate"] --- # FEDOT vs FATE *GraphCanon updated Aug 4, 2026* ## Verdict Pick FEDOT if fEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction; pick FATE if fATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. [FEDOT](https://fedot.readthedocs.io) reports 709 GitHub stars, 94 forks, and 82 open issues, last pushed Jul 27, 2026. [FATE](https://github.com/FederatedAI/FATE) has 6.1k stars, 1.6k forks, and 21 open issues, last pushed Nov 19, 2024. Figures are from public GitHub metadata via [FEDOT's repository](https://github.com/aimclub/FEDOT) and [FATE's repository](https://github.com/FederatedAI/FATE). | | [FEDOT](/tools/aimclub-fedot.md) | [FATE](/tools/federatedai-fate.md) | | --- | --- | --- | | Tagline | Automated modeling and machine learning framework FEDOT | An Industrial Grade Federated Learning Framework | | Stars | 709 | 6,089 | | Forks | 94 | 1,568 | | Open issues | 82 | 21 | | Language | Python | Python | | Adopt for | FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. | FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes. | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users. | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FEDOT](/tools/aimclub-fedot.md) | [FATE](/tools/federatedai-fate.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 7d | 623d | | Open issues (now) | 82 | 21 | | Full report | [trust report](/tools/aimclub-fedot/trust.md) | [trust report](/tools/federatedai-fate/trust.md) | ## Decision facts: FEDOT - **Adopt for:** FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction. ## 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. ## Choose when ### Choose FEDOT if… - License: FEDOT is BSD-3-Clause, FATE is Apache-2.0. - Tags unique to FEDOT: automl, evolutionary-algorithms, genetic-programming, hyperparameter-optimization. - For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types ### Choose FATE if… - License: FATE is Apache-2.0, FEDOT is BSD-3-Clause. - 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 NOT to use FEDOT - In scenarios with strict real-time requirements due to its optimization time - For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs - When the project specifically requires a non-evolutionary approach for pipeline design ## 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 ## Common questions ### What is the difference between FEDOT and FATE? FEDOT: Automated modeling and machine learning framework FEDOT. FATE: An Industrial Grade Federated Learning Framework. See the comparison table for live GitHub stats and shared categories. ### When should I choose FEDOT over FATE? Choose FEDOT over FATE when License: FEDOT is BSD-3-Clause, FATE is Apache-2.0; Tags unique to FEDOT: automl, evolutionary-algorithms, genetic-programming, hyperparameter-optimization; For projects requiring automated generative design of machine-learning pipelines suitable for a wide range of tasks and data types. ### When should I choose FATE over FEDOT? Choose FATE over FEDOT when License: FATE is Apache-2.0, FEDOT is BSD-3-Clause; 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 avoid FEDOT? In scenarios with strict real-time requirements due to its optimization time For environments where only specific ML libraries are acceptable and FEDOT's integrations do not meet those needs When the project specifically requires a non-evolutionary approach for pipeline design ### 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 ### Is FEDOT or FATE more popular on GitHub? FATE has more GitHub stars (6,089 vs 709). Stars measure visibility, not whether either tool fits your constraints. ### Are FEDOT and FATE open source? Yes - both are open-source projects on GitHub (FEDOT: BSD-3-Clause, FATE: Apache-2.0). ### Where can I find alternatives to FEDOT or FATE? GraphCanon lists graph-backed alternatives at [FEDOT alternatives](/tools/aimclub-fedot/alternatives) and [FATE alternatives](/tools/federatedai-fate/alternatives) ([FEDOT markdown twin](/tools/aimclub-fedot/alternatives.md), [FATE markdown twin](/tools/federatedai-fate/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/aimclub-fedot-vs-federatedai-fate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FEDOT or FATE? FEDOT: Active. FATE: Dormant. 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 FEDOT and FATE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FEDOT trust report](/tools/aimclub-fedot/trust); [FATE trust report](/tools/federatedai-fate/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aimclub-fedot`](/api/graphcanon/graph?tool=aimclub-fedot) - 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/_