Home/Compare/FEDOT vs FATE

Comparison

FEDOT vs FATE

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.

Markdown twin · FEDOT alternatives · FATE alternatives

GraphCanon updated 2w

FEDOT logo

FEDOT

aimclub/FEDOT

709pushed Jul 27, 2026
vs
FATE logo

FATE

FederatedAI/FATE

6.1kpushed Nov 19, 2024

Trust & integrity

SignalFEDOTFATE
Maintenance
Active (7d since push)
As of 2w · github_public_v1
Dormant (623d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
Published findings
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1

Tagline

FEDOT
Automated modeling and machine learning framework FEDOT
FATE
An Industrial Grade Federated Learning Framework

Stars

FEDOT
709
FATE
6.1k

Forks

FEDOT
94
FATE
1.6k

Open issues

FEDOT
82
FATE
21

Language

FEDOT
Python
FATE
Python

Adopt for

FEDOT
FEDOT: auto-generates ML pipelines using evolutionary algorithms, supports various tasks including classification, regression, clustering, time series prediction.
FATE
FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.

Persona

FEDOT
-
FATE
-

Runtime

FEDOT
-
FATE
-

License

FEDOT
BSD-3-Clause
FATE
Apache-2.0 License permits use, study, sharing, and modification with few conditions but no warranty given to users.

Last pushed

FEDOT
Jul 27, 2026
FATE
Nov 19, 2024

Categories

FEDOT
Model Training
FATE
Model Training

Trust and health

Maintenance

FEDOT
Active (82%)
FATE
Dormant (18%)

Days since push

FEDOT
7d
FATE
623d

Open issues (now)

FEDOT
82
FATE
21

OSV dependency advisories

FEDOT
Published findings
FATE
No lockfile (source not queried)

Full report

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

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

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 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

Explore

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: FEDOT 709 · FATE 6.1k (synced Aug 4, 2026).

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 and FATE alternatives (FEDOT markdown twin, FATE markdown twin), 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 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; FATE trust report.

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