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The need

Collaborative Analysis

Answering many of the questions in health care often requires incorporating data that are located at different sources. Typically, data from different parties are analyzed by centralizing them. That is, data are brought to where the algorithms are. Unfortunately, this raises heavy concerns regarding the privacy and security of sensitive patient data.

The solution

Federated Learning

Federated learning has emerged as a technological solution to address these concerns. In this novel paradigm, algorithms are brought to where the data are. This way, we can maximize the potential of multiple datasets while minimizing data leaks and privacy risks.

The tool

vantage6

This led us to create vantage6, our open-source platform for supporting the development of federated learning projects.

Philosophy

vantage6 is built on top of three pillars

Infrastructure

Algorithms

Data

...using three basic principles

Autonomy

Each party is in control of their own data.

Heterogeneity

Parties are allowed to have differences in hardware and operating systems.

Flexibility

Support for horizontally- and vertically-partitioned data.

Latest News

News of interest to the community

Unauthorized access to our vantage6 image registry

April 2, 2026

We are hereby reporting unauthorized access to the community Harbor image registry. A malicious user has gained admin access to the application and injected a malicious binary into some of the images.