Federated Learning Frameworks for Robust Privacy Preservation in Healthcare Data Analytics

Abstract

Digital health technologies have resulted in a steep growth in healthcare data, which is problematic in terms of privacy and security as well as regulatory compliance. Federated learning (FL) is an example of a decentralized machine learning system that offers a good solution to privacy-compliant data analytics through collaborative learning among multiple institutions without exchanging raw data. The paper will discuss the potential of FL frameworks in healthcare data analytics in terms of privacy preservation, data security, and model performance. Th paper discuss the major advantages of FL in keeping patient confidentiality with the use of multi-institutional data to enhance predictive models in clinical decision-making. Synthetic healthcare experiments show that FL can match the strength of centralized models in model quality and also ensure that a single institution has control of its data. The findings indicate that federated models are effective in accuracy (up to 95%) in disease prediction tasks, having lower risks of data leakages. Furthermore, FL models address the privacy issue by making sure that vulnerable patient information is not disclosed to third-party servers. The conclusion of the paper is that FL frameworks could provide a realistic solution to privacy-friendly healthcare data analytics and could transform the sphere of data-sharing in healthcare without affecting patient confidentiality. Nevertheless, issues that are associated with model convergence, model communication, and regulatory compliance are some of the areas where researchers and other stakeholders can conduct new research and enhance them.