Case Study: Healthcare

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

In this white paper, you will learn:

How you can preserve intellectual property while generating value from sensitive data
Deep dive into federated and secure QSAR models
How Apheris prevents uncontrolled data sharing
How you can pave the way to precision medicine with federated and secure QSAR model training

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

In this white paper, you will learn:

About vertically distributed data
Workflow of privacy-preserving data science
Deep-dive into privacy-preserving record linkage
Deep-dive into federated and privacy-preserving analytics

In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.