Securing Healthcare Data: Federated Learning for Privacy-Preserving AI in Medical Applications

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Securing Healthcare Data: Federated Learning for Privacy-Preserving AI in Medical Applications

Popuri, Venkatesh (2024) Securing Healthcare Data: Federated Learning for Privacy-Preserving AI in Medical Applications. International Journal of Management Technology, 11 (3). pp. 64-82. ISSN 2055-0847(Print) ,2055-0855(Online)

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Abstract

Federated Learning (FL) is a technique used when sharing raw data cannot be done because of privacy laws. FL is used to train machine learning algorithms on decentralized data. Electronic health records, which hold private patient data, are one type of such data. In FL, local models are trained, and the model parameters are then combined on a central server instead of sharing sensitive data. But this approach poses privacy risks, so before disclosing the model parameters, privacy protection measures such data confidentiality must be put in existence. During the pandemic, there is a need to improve the healthcare system. Numerous advancements in Artificial Intelligence (AI) technology are continuously being utilized in several healthcare domains. Federated Learning (FL), one such development, has gained popularity mostly because of its decentralized, cooperative approach to creating AI models. Since integrating privacy algorithms can affect the utility, it is important to strike a balance when it comes to privacy and utility in FL research. The goal is to use strategies such as data generalizing, feature selection for reducing dimensions, and reduction in the confidentiality process to maximize FL's effectiveness while preserving privacy. To create a predictive model for healthcare applications, this study also explores the idea of segmenting data based on attributes rather than records. It assesses the effectiveness of the model recommended by utilizing actual medical data.

Item Type:	Article
Subjects:	H Social Sciences > H Social Sciences (General) T Technology > T Technology (General)
Depositing User:	Professor Mark T. Owen
Date Deposited:	10 Aug 2024 13:42
Last Modified:	10 Aug 2024 13:42
URI:	https://tudr.org/id/eprint/3286

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