Skip to main content

Vantage6 client

Project description

<img src=”https://github.com/IKNL/guidelines/blob/master/resources/logos/vantage6.png?raw=true” width=200 align=”right”>

[![Coverage Status](https://coveralls.io/repos/github/IKNL/ppDLI/badge.svg?branch=master)](https://coveralls.io/github/IKNL/ppDLI?branch=master)

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/bcde6ed5c77440c6969462bfead0774c)](https://app.codacy.com/app/frankcorneliusmartin/ppDLI?utm_source=github.com&utm_medium=referral&utm_content=IKNL/ppDLI&utm_campaign=Badge_Grade_Dashboard)

[![PyPI version](https://badge.fury.io/py/ppDLI.svg)](https://badge.fury.io/py/ppDLI)

[![Build Status](https://travis-ci.org/IKNL/ppDLI.svg?branch=master)](https://travis-ci.org/IKNL/ppDLI)

[![DOI](https://zenodo.org/badge/120275991.svg)](https://zenodo.org/badge/latestdoi/120275991)

# Introduction

The growing complexity of cancer diagnosis and treatment requires data sets that are larger than currently available in a single hospital or even in cancer registries. However, sharing patient data is difficult due to patient privacy and data protection needs. Federated learning technology has the potential to overcome these limitations. In this approach, organizations can collaborate by exchanging aggregated data and/or statistics while keeping the underlying data safely on site and undisclosed. This repository contains software (and instructions) to setup a federated learning infrastructure.

For an overview of the architecture and information on how to use the infrastructure, please see [https://vantage6.ai](https://vantage6.ai). For documentation, please see [https://docs.distributedlearning.ai/](https://docs.distributedlearning.ai/).

## Installation

See the [documentation](https://docs.distributedlearning.ai/) for detailed instructions on how to install the server and nodes.

Platform: UNKNOWN Requires-Python: >=3.6 Description-Content-Type: text/markdown

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

vantage6-client-1.0.0b6.tar.gz (10.9 kB view details)

Uploaded Source

Built Distribution

vantage6_client-1.0.0b6-py3-none-any.whl (15.3 kB view details)

Uploaded Python 3

File details

Details for the file vantage6-client-1.0.0b6.tar.gz.

File metadata

  • Download URL: vantage6-client-1.0.0b6.tar.gz
  • Upload date:
  • Size: 10.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0.post20200309 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.6

File hashes

Hashes for vantage6-client-1.0.0b6.tar.gz
Algorithm Hash digest
SHA256 64d3b848715d9203fc77bbcb883cdde1fddf4c58827528554518f77b79b3e6a8
MD5 f87e0ad43f6ab2c05321998fd51a9489
BLAKE2b-256 68fe8b981efeb5a0f841ad6a43b0bf96b450d55dbe0665f8406ade752c6691a9

See more details on using hashes here.

File details

Details for the file vantage6_client-1.0.0b6-py3-none-any.whl.

File metadata

  • Download URL: vantage6_client-1.0.0b6-py3-none-any.whl
  • Upload date:
  • Size: 15.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0.post20200309 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.7.6

File hashes

Hashes for vantage6_client-1.0.0b6-py3-none-any.whl
Algorithm Hash digest
SHA256 b5420b6b5d8bce56c53a43f796863e52829f93d61fa2daa441b97425c22dff34
MD5 804c4f5599c47aa7e1dddf5ac0a69aef
BLAKE2b-256 254cda444652053c416125b56085426f93f62707d1a3c0be0bc5ae1b83861dde

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page