Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study.

ABSTRACT:

Background

Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual's record is scattered among different sites.

Objective

The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data.

Methods

We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. F

SUBMITTER: Cha D 

PROVIDER: S-EPMC8262549 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature