Federated Learning in Healthcare is the Future, But the Problems Are Contemporary

Mustafa Topaloglu, Elisabeth Morrell, Umit Topaloglu

2021

Abstract

Federated Learning (FL) has originated out of a need to mitigate certain inherent limitations of ML, particularly the capability to train on larger datasets for improved performance, which is typically an unwieldy coordination for an inter-institutional collaboration due to existing patient protection laws and regulations. FL may also play a crucial role in bypassing ML’s innate algorithmic discrimination issues via the access of underrepresented groups’ data spanning across geographically distributed institutions and the diverse populations. FL inherits many of the difficulties of ML and as such we have discussed two pressing FL challenges, namely: privacy of the model exchange as well as equity and contribution considerations.

Paper Citation

in Harvard Style

Topaloglu M., Morrell E. and Topaloglu U. (2021). Federated Learning in Healthcare is the Future, But the Problems Are Contemporary. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS, ISBN 978-989-758-536-4, pages 593-598. DOI: 10.5220/0010722600003058

in Bibtex Style

@conference{dmmlacs21,
author={Mustafa Topaloglu and Elisabeth Morrell and Umit Topaloglu},
title={Federated Learning in Healthcare is the Future, But the Problems Are Contemporary},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,},
year={2021},
pages={593-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010722600003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: DMMLACS,
TI - Federated Learning in Healthcare is the Future, But the Problems Are Contemporary
SN - 978-989-758-536-4
AU - Topaloglu M.
AU - Morrell E.
AU - Topaloglu U.
PY - 2021
SP - 593
EP - 598
DO - 10.5220/0010722600003058