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Conference Paper Federated Learning in Prediction of Dementia Stage: An Experimental Study
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Authors
Boyun Eom, Muhammad Zubair, Dong-Hwan Park, Hyunhak Kim, Young-Ho Suh, Sunhwan Lim, Chanwon Park
Issue Date
2023-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2023, pp.1785-1788
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC58733.2023.10392680
Abstract
Federated Learning(FL) has emerged as the optimal approach for training machine learning models when dealing with data containing sensitive information, making data sharing impractical. Particularly in contexts where privacy is a primary concern, such as medical applications, Federated Learning demonstrates its efficacy as a solution. Motivated by this, we have conducted comprehensive experiments using a medical image dataset. One of the key objectives of these experiments is to evaluate the influence of Non-IID data which is frequently encountered in Federated Learning, especially within the medial field. We present our exploration of Federated Learning in classification of OASIS medical images, along with the results obtained from various experiments.
KSP Keywords
An experimental study, Data sharing, Federated learning, Image datasets, Medical Applications, Medical Image, Sensitive information, machine learning models