Federated Learning on Clinical Benchmark Data: Performance Assessment.

ABSTRACT:

Background

Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas.

Objective

The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset.

Methods

To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Me

SUBMITTER: Lee GH 

PROVIDER: S-EPMC7652692 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature