PySyft

Perform data science on data that remains in someone else's server

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Builder

OpenMined

OpenMined • individual

Stars

9,870

Using upstream star count

Forks

2,005

Using upstream fork count

Open Issues

Activity Score

0/100

0 commits in 30d

Created

Jul 18, 2017

Project creation date

README Summary

PySyft is a Python library that enables secure, private data science by allowing computation on data without requiring direct access to it. The framework supports federated learning and differential privacy techniques, enabling researchers and data scientists to perform analysis on sensitive data that remains on remote servers. It provides tools for privacy-preserving machine learning across multiple parties while maintaining data sovereignty.

AI Dev Skills

Unmapped

Tags

Federated LearningDifferential PrivacySecure Multi-Party ComputationHomomorphic EncryptionPrivacy-Preserving Machine LearningCryptographic ProtocolsDistributed ComputingRemote ExecutionData GovernanceCross-organizational model trainingFinTechHealthcareAI SafetyDecentralized AITrustworthy AITextInsuranceTabularDistributedRegulatory-compliant machine learningCloudMultimodalOn-premisePharmaceuticalsMedical research on sensitive patient dataPrivacy-Preserving AICollaborative AI without data sharingImageGovernmentSelf-hostedFinancial fraud detection across institutionsPython

Recent Activity

Updated 9 months ago

7 Days

30 Days

90 Days

Quality

beta

Quality: high
Maturity: beta

PM Skills

Scale & ReliabilityDeveloper Platform

Languages

Python100.0%

Timeline

Project created: Jul 18, 2017
Forked: Mar 22, 2026
Your last push: 9 months ago
Upstream last push: 9 months ago
Tracked since: Jul 15, 2025

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