Studying The Effectiveness of Federated Learning in The Healthcare Domain
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Date
2023-06-10
Journal Title
Journal ISSN
Volume Title
Publisher
University of M'sila
Abstract
Federated Learning (FL) has emerged as a promising approach for training Machine Learning
(ML) models in decentralized settings, such as Smart Healthcare Systems (SHSs). Adoption of
FL in SHSs provides many advantages especially the preservation of patient’s privacy.
This project aims to investigate the effectiveness of FL in the context of SHSs by comparing it
with the traditional data-centralized approach. Specifically, four FL strategies are implemented
using the Flower framework, with each strategy trained on four different types of datasets: brain
tumor, pneumonia, Alzheimer's disease, and COVID-19. The performance of these FL strategies
is then compared to that of a data-centralized model trained on the same datasets.
Our obtained experimental results reveal that the accuracy of the FL models is in general less
than the data-centralized model for all four strategies. Such disparity in accuracy is explained by
the challenges associated with decentralized FL learning in SHS settings.
Nevertheless, such a small extent of difference can be tolerated considering the benefits of FL
brought to SHSs. Thus, we may conclude that there should be a trade-off between high accuracy
and FL advantages.
Description
Keywords
Machine Learning (ML), Federated Learning (FL), Smart Healthcare Systems (SHSs), Data Privacy, Decentralized Learning, Models Aggregation Methods.