GraphCanon updated 2w · GitHub synced 2w
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
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (623d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
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 · Aug 4, 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. It is for simple testing purposes. Refer to this guide.
Cluster deployment
Deploying FATE to multiple nodes to achieve scalability, reliability and manageability.
- Cluster deployment by CLI: Using CLI to deploy a FATE cluster.
- Docker-Compose deployment: Using docker-compose to deploy FATE.
Quick Start
- Training Demo with Only FATE Installed From Pypi
- Training Demo with Both FATE AND FATE-Flow Installed From Pypi
For agents
This page has a .md twin and JSON over the API.