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 the 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 in 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.

Survey of Medical Applications of Federated Learning

“…This trend demonstrates a growing interest in FL research within the medical field. Table 1 provides a summary of these studies, categorizing them by their target diseases and the types of data utilized [ 21 – 78 ].…”

“…Most local models for medical FL research were neural networks, while very few were machine learning models. Considering that certain types of medical data, such as laboratory results, are captured in tabular formats that exhibit low data complexity [69][70][71][72][73][74][75][76][77][78], there is a need for FL research that utilizes machine learning. Machine learning models typically have lower complexity than neural network models and could be more suitable for these types of data.…”

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

“…The framework ensures privacy for FL using differential privacy (DP). In [126], the authors applied DP mechanisms through feature selection based on the statistical methods in the FL model to enhance privacy, analyze patients' genomic data, and identify the risk of heart failure. Zhao et al [127] applied the DP technology in FL to ensure data security and privacy by adding Gaussian noise during the local training and model aggregation process.…”

“…Islam et al [126] proposed an FL model to analyze patients' genomic data and identify the risk of heart failure. To enhance the privacy preservation of patients' private data while sharing them among collaborating healthcare organizations in the FL framework, the authors applied DP mechanisms by using feature selection based on statistical methods to increase scalability and accuracy in federated settings where data are vertically partitioned.…”

Federated Learning-Based Privacy-Preserving Data Aggregation Scheme for IIoT

“…During local model training, the central manager can access all local model updates and share model data with other servers in the aggregation area. The global model in the central manager is then updated and shared back to the local device for further training [22]. FL is a typical example of machine learning and analysis on mobile wearable devices through 5G and later wireless networks, which have been deployed to sensitive healthcare applications [23].…”