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Good that you are here! |
{% hint style="danger" %} Check out our new documentation\ \ This documentation space is no longer maintained. Please find the latest documentation at https://docs.vantage6.ai! {% endhint %}
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This work was presented as a contribution during the AMIA 2020 Virtual Annual Symposium.
It was accompanied by an oral presentation, which you can watch right here as well (~9 min, in English)
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Vantage6 stands for privacy preserving federated learning infrastructure for secure insight exchange.
The project is inspired by the Personal Health Train (PHT) concept. In this analogy vantage6 is the tracks and stations. Compatible algorithms are the trains, and computation tasks are the journey.
vantage6 is here for:
- delivering algorithms to data stations and collecting their results
- managing users, organizations, collaborations, computation tasks and their results
- providing control (security) at the data-stations to their owners
vantage6 is not (yet):
- formatting the data at the data station
- aligning data across the data stations
- a finished/polished product
vantage6 is designed with three fundamental functional aspects of Federated learning.
- Autonomy. All involved parties should remain independent and autonomous.
- Heterogeneity. Parties should be allowed to have differences in hardware and operating systems.
- Flexibility. Related to the latter, a federated learning infrastructure should not limit the use of relevant data.
Documentation
- docs.vantage6.ai -> this documentation
- tech-docs.vantage6.ai -> unfinished technical documentation
- vantage6.ai -> general vantage6 website
- academic paper -> technical insights into vantage6
Source code
- vantage6 -> contains all components (and the python-client).
- Planning -> contains all features, bugfixes and feature request we are working on. To submit one yourself, you can create a new issue.
{% hint style="warning" %} The old/previous (seperated) repositories can still be found at the IKNL Github in archived form:
- vantage6-master -> contains all other repositories, used for synchronization and releasing
- vantage6-node -> node source code
- vantage6-server -> server source code
- vantage6-client -> (python) client source code
- vantage6-common -> common functionality {% endhint %}
Community
- Discourse -> discussion platform, ask anything here
- Discord -> for if you prefer a quick chat with the developers
This documentation space is intended for users of the vantage6 solution. You will find information on how to setup your own federated learning network, and how to maintain and interact with it.
Here you will not find:
- in depth technical documentation
- background on federated learning
Vantage6 is completely open source under the Apache License.
If you want to join, find us on our Discord channel.