Date of Award
2023
Document Type
Thesis
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Zhu, Xingquan
Abstract
Machine learning (ML) has traditionally been used to make predictive models by training on local data. However, due to concerns regarding privacy, it is not always possible to collect and combine data from different sources. On the other hand, if there are insufficient data available, it might not be possible to construct accurate models to produce meaningful outcomes. This is where Federated Learning comes to the rescue. Federated Learning (FL) represents a sophisticated distributed machine learning strategy that enables multiple devices hosted at different institutions such as hospitals, to collaboratively train a global model while ensuring that their respective data remains securely stored on-premises. It addresses privacy concerns and data protection regulations, because raw data does not need to be shared or centralized during the training process. This thesis research studies how two different FL architectures, centralized and decentralized FL, affect medical image classification. To study and validate the findings, skin cancer images dataset is used in a federated learning setting with five sites/clients, and a center for centralized FL. Experimental results show that using both centralized and decentralized (peer to peer) version of FL for classification of skin cancer images outperforms using the traditional ML. In addition, two different FL settings, centralized federated learning (CFL) and decentralized federated learning (DFL), are compared using different data distributions across sites/clients. Our study shows that the best accuracy (95.14%) was achieved with the DFL model when tested on the original dataset (without adding bias to the class distributions). This asserts that class distribution imbalance between sites has a significant impact to the federated learning.
Extent
75 p.
Recommended Citation
Blazanovic, Danica, "FEDERATED LEARNING FOR MEDICAL IMAGE CLASSIFICATION" (2023). Electronic Theses and Dissertations 2020 - Present. 1185.
https://digitalcommons.fau.edu/etd_general/1185
Identifier
FA00014205
Comments
Includes bibliography.|Thesis (MS)--Florida Atlantic University, 2023.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.