# FederatedAI/FATE

An Industrial Grade Federated Learning Framework

Repository: https://github.com/FederatedAI/FATE
Canonical: https://ross.abutalabs.com/products/fate
Language: Python
License: Apache-2.0
License Family: permissive
Topics: fate, machine-learning, federated-learning, privacy-preserving, algorithm
Last push: 2024-11-19T08:19:11+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2778, "days_push": 652, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6089, forks 1571 (observed 2026-08-28T04:09:34.959636+00:00)

## What it is
FATE (Federated AI Technology Enabler) is an industrial-grade open-source federated learning framework hosted by the Linux Foundation. It enables organizations to collaboratively train machine learning models while protecting data privacy using homomorphic encryption and multi-party computation protocols.

## Use cases
- train models across organizations without sharing raw data
- run privacy-preserving logistic regression on distributed datasets
- set up a federated learning cluster for enterprise collaboration
- apply homomorphic encryption to machine learning pipelines
- perform federated transfer learning between parties
- experiment with secure multi-party computation for ML

## When to choose
- you need production-grade federated learning with strong privacy guarantees
- multiple parties must collaborate on model training under data protection regulations
- you want a mature framework with many federated algorithms (LR, tree-based, deep learning)
- you need cluster-scale deployment with scalability and reliability

## When to avoid
- you only need simple centralized machine learning without privacy constraints
- your team cannot manage the operational complexity of multi-node deployments
- you need lightweight federated learning for mobile/edge devices with minimal overhead

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, llm-training, security, cryptography, data-science
- domain: machine-learning, privacy, artificial-intelligence, data-science, security
- platform: python, cross-platform
- tags: federated-learning, privacy-preserving, homomorphic-encryption, multi-party-computation, distributed-training, docker, linux

## Member repositories
- FederatedAI/FATE (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:34.959636+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:48:17.901761+00:00, confidence not recorded.
  - readme: https://github.com/FederatedAI/FATE (fetched 2026-08-28T04:09:34.959636+00:00, sha 6007500691a8)
- Data as of 2026-08-30T08:39:29.467469+00:00.