Pistoia Alliance April Virtual Conference: Federated Machine Learning On Heterogenous Drug Discovery Data
This on-demand recording is part of the Pistoia Alliance Conference: Collaborative R&D in Action, April 20-23, 2021. For more information about related events, please visit our online calendar.
Data relevant for building robust drug discovery models is distributed within and across companies. Federated Learning is a machine learning technique to train an algorithm across multiple decentralized datasets which increasingly finds adoption.
In this session, we will take a look at different techniques to solve one of the biggest bottlenecks of federated data set-ups: heterogenous datasets.
We will discuss how data can be normalized and harmonized across different parties before training and how to train machine learning models on data of different modalities.
CEO, Apheris