Privacy-Preserving Federated Learning model for healthcare data

Abstract

Federated Learning (FL) is a method for training machine learning algorithms on decentralized data where sharing raw data is not feasible due to privacy regulations. An instance of such data is Electronic Health Records (EHRs), which contain confidential patient information. In FL, the sensitive data is not shared, rather local models are trained and the model parameters are then aggregated on a central server. However, this method presents privacy challenges, necessitating the implementation of privacy protection strategies, such as data anonymization, before sharing the model parameters. Balancing the trade-off between privacy and utility is a crucial aspect of FL research, as integrating privacy algorithms can have an impact on the utility. The objective of this thesis is to improve the performance of FL while maintaining privacy, through techniques like data generalization, feature selection for dimension reduction, and minimizing noise in the anonymization process. This research also investigates separating data based on features instead of records and evaluates the performance of the proposed model using real healthcare data, with the aim of developing a predictive model for healthcare applications.