FederatedAI/FATE

Health v2 · maintenance only

Adoption not part of the score

6089 stars · 1571 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

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

framework · maturity active

machine-learning llm-training security cryptography data-science machine-learning privacy artificial-intelligence data-science security python cross-platform federated-learning privacy-preserving homomorphic-encryption multi-party-computation distributed-training docker linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="FederatedAI/FATE")