Privacy-Preserving Federated Learning in Healthcare

Sung Hwan Moon; Won Hee Lee

doi:10.1109/ICEIC57457.2023.10049966

Privacy-Preserving Federated Learning in Healthcare

Sung Hwan Moon, Won Hee Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Citations (Scopus)

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 language	English
Title of host publication	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350320213
DOIs	https://doi.org/10.1109/ICEIC57457.2023.10049966
Publication status	Published - 2023
Event	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 - Singapore, Singapore Duration: 5 Feb 2023 → 8 Feb 2023

Publication series

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

Conference

Conference	2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
Country/Territory	Singapore
City	Singapore
Period	5/02/23 → 8/02/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

Artificial intelligence
federated learning
healthcare
privacy-preserving

Access to Document

10.1109/ICEIC57457.2023.10049966

Cite this

Moon, S. H., & Hee Lee, W. (2023). Privacy-Preserving Federated Learning in Healthcare. In 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEIC57457.2023.10049966

@inproceedings{21b0fd2549a74ad6a1e3e665779f03e5,

title = "Privacy-Preserving Federated Learning in Healthcare",

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.",

keywords = "Artificial intelligence, federated learning, healthcare, privacy-preserving",

author = "Moon, {Sung Hwan} and {Hee Lee}, Won",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 ; Conference date: 05-02-2023 Through 08-02-2023",

year = "2023",

doi = "10.1109/ICEIC57457.2023.10049966",

language = "English",

series = "2023 International Conference on Electronics, Information, and Communication, ICEIC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2023 International Conference on Electronics, Information, and Communication, ICEIC 2023",

address = "United States",

}

Moon, SH & Hee Lee, W 2023, Privacy-Preserving Federated Learning in Healthcare. in 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023, Singapore, Singapore, 5/02/23. https://doi.org/10.1109/ICEIC57457.2023.10049966

Privacy-Preserving Federated Learning in Healthcare. / Moon, Sung Hwan; Hee Lee, Won.
2023 International Conference on Electronics, Information, and Communication, ICEIC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (2023 International Conference on Electronics, Information, and Communication, ICEIC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Privacy-Preserving Federated Learning in Healthcare

AU - Moon, Sung Hwan

AU - Hee Lee, Won

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - federated learning

KW - healthcare

KW - privacy-preserving

UR - http://www.scopus.com/inward/record.url?scp=85150463556&partnerID=8YFLogxK

U2 - 10.1109/ICEIC57457.2023.10049966

DO - 10.1109/ICEIC57457.2023.10049966

M3 - Conference contribution

AN - SCOPUS:85150463556

T3 - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

BT - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023

Y2 - 5 February 2023 through 8 February 2023

ER -

Privacy-Preserving Federated Learning in Healthcare

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this