@inproceedings{bc56878885564801a8929d329684520d,
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",
}