# OpenMined/PySyft

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

Repository: https://github.com/OpenMined/PySyft
Canonical: https://ross.abutalabs.com/products/pysyft
Homepage: https://www.openmined.org/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: deep-learning, secure-computation, pytorch, privacy, cryptography, python, syft, federated-learning, hacktoberfest
Last push: 2026-08-26T18:58:33+00:00

## Health v2 (maintenance only)
Score: 74/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 28, longevity 100
- inputs: {"age_days": 3333, "days_push": 7, "days_rel": 566, "gap_med": 55, "n_releases_24m": 4}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9957, forks 1999 (observed 2026-08-28T04:10:38.970024+00:00)

## What it is
PySyft is a Python library that lets data scientists run computations on private data that stays on the data owner's server, with results shared only after owner approval. It is part of the OpenMined ecosystem for privacy-preserving, federated, and secure multi-party computation.

## Use cases
- run data science on private data without moving it
- federated learning across data silos
- secure multi-party computation in python
- analyze sensitive medical or genomic data remotely
- let auditors query models without exposing weights
- collaborate on datasets under access control

## When to choose
- you need to compute on data you cannot or should not copy
- data owners must approve every analysis before results are shared
- you want privacy-preserving machine learning with PyTorch

## When to avoid
- you just need standard ML on data you already own
- you need the legacy PySyft ≤0.9 API without pinning versions
- you require a turnkey enterprise product rather than an open-source framework

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, cryptography, security, privacy, data-science, sdk
- domain: privacy, machine-learning, data-science, security, healthcare, large-language-models
- platform: python, cross-platform, cloud
- tags: federated-learning, secure-computation, privacy-preserving, remote-data-science, data-sovereignty, pytorch, openmined

## Member repositories
- OpenMined/PySyft (main) score 74

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:38.970024+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 2026-08-29T17:20:12.188339+00:00, confidence not recorded.
  - readme: https://github.com/OpenMined/PySyft (fetched 2026-08-28T04:10:38.970024+00:00, sha 9b5d4532a6c7)
  - homepage: https://www.openmined.org/ (fetched 2026-08-29T08:19:58.871279+00:00, sha 0144696600ab)
  - registry_pypi: https://pypi.org/pypi/pysyft/json (fetched 2026-08-29T08:19:58.882037+00:00, sha bb64609d9c88)
  - site_page: https://openmined.org/foundation (fetched 2026-08-29T08:19:58.880291+00:00, sha bf8109360749)
- Data as of 2026-08-30T08:39:29.467469+00:00.