GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
FATE is an industrial-grade framework for federated learning focused on privacy-preserving model training across single or multiple nodes.
Good fit when
- When needing secure multi-party computation to train machine-learning models across distributed data without sharing sensitive information
- For scalability and reliability in environments with high privacy requirements
Avoid when
- 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
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (682d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install FATESimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
FederatedAI/FATE is an industrial-grade framework for federated learning that supports deployment on single and multiple nodes with privacy-preserving capabilities.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Oct 3, 2026
Categories
Tags
README
Getting Started FATE can be deployed on a single node or on multiple nodes. Choose the deployment approach which matches your environment. Release version can be downloaded here. Standalone deployment Deploying FATE on a single node via PyPI, pre built docker images or installers...
For agents
This page has a .md twin and JSON over the API.