Privacy-Preserving Federated Learning in Healthcare

Sung Hwan Moon, Won Hee Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

Federated learning (FL) has received great attention in healthcare primarily due to its decentralized, collaborative nature of building a machine learning (ML) model. Over the years, the FL approach has been successfully applied for enhancing privacy preservation in medical ML applications. This study aims to review prevailing applications in healthcare for the future landing FL application. We identified the emerging applications of FL in key healthcare domains, including COVID-19, brain tumor segmentation, mammogram, sleep quality prediction, and smart healthcare system. Finally, we discuss privacy concerns in federated setting and provide current methods to increase the data privacy capabilities of FL.

Original languageEnglish
Title of host publication2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350320213
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 - Singapore, Singapore
Duration: 5 Feb 20238 Feb 2023

Publication series

Name2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

Conference

Conference2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Country/TerritorySingapore
CitySingapore
Period5/02/238/02/23

Keywords

  • Artificial intelligence
  • federated learning
  • healthcare
  • privacy-preserving

Fingerprint

Dive into the research topics of 'Privacy-Preserving Federated Learning in Healthcare'. Together they form a unique fingerprint.

Cite this