OpenMined/PySyft
Perform data science on data that remains in someone else's server
Supports arbitrary Python code execution and third-party libraries directly against remote datasets through a client-server architecture, with data owners enforcing access controls via structured transparency policies. Deploys as containerized Datasite servers (Docker/Kubernetes) that researchers query via Jupyter notebooks, managing code requests and results retrieval without exposing underlying data. Integrates with standard data science workflows through a Python SDK with APIs for datasets, user management, policy enforcement, and audit trails.
9,863 stars and 101 monthly downloads. No commits in the last 6 months. Available on PyPI.
Stars
9,863
Forks
2,006
Language
Python
License
Apache-2.0
Category
Last pushed
Jul 15, 2025
Monthly downloads
101
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/OpenMined/PySyft"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
flwrlabs/flower
Flower: A Friendly Federated AI Framework
zama-ai/concrete-ml
Concrete ML: Privacy Preserving ML framework using Fully Homomorphic Encryption (FHE), built on...
p2pfl/p2pfl
P2PFL is a decentralized federated learning library that enables federated learning on...
JonasGeiping/breaching
Breaching privacy in federated learning scenarios for vision and text
SMILELab-FL/FedLab
A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.