FATE

An Industrial Grade Federated Learning Framework

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Builder

FederatedAI

FederatedAI • individual

Stars

6,059

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Forks

1,566

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Activity Score

0/100

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Created

Jan 24, 2019

Project creation date

README Summary

FATE is an industrial-grade federated learning framework that enables secure multi-party machine learning without exposing raw data. It provides a comprehensive suite of federated learning algorithms, privacy-preserving techniques, and deployment tools for enterprise environments. The framework supports heterogeneous federated learning scenarios and includes components for model training, evaluation, and serving.

AI Dev Skills

Unmapped

Tags

Federated LearningPrivacy-Preserving Machine LearningSecure Multi-Party ComputationHomomorphic EncryptionDifferential PrivacyDistributed SystemsMachine Learning Operations (MLOps)Cryptographic ProtocolsData Privacy EngineeringMulti-Party Machine LearningBankingInsurancePrivacy-Preserving AIMulti-hospital Medical ResearchImageCross-institutional Model TrainingCollaborative Machine LearningMulti-cloudHealthcareTelecommunicationsCloudDistributed AIHybrid CloudTabularOn-premiseFinTechPrivacy-Preserving Credit ScoringGovernmentAI GovernanceDistributed Risk AssessmentTextCollaborative Fraud DetectionPrivacy-Compliant Customer AnalyticsResponsible AIPython

Recent Activity

Updated 1 years ago

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Quality

production

Quality: high
Maturity: production

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Python100.0%

Timeline

Project created: Jan 24, 2019
Forked: Mar 22, 2026
Your last push: 1 years ago
Upstream last push: 1 years ago
Tracked since: Nov 19, 2024

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