A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems

Xin Gu, Fariza Sabrina, Zongwen Fan () and Shaleeza Sohail
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Xin Gu: School of Information Technology, King’s Own Institute, Sydney, NSW 2000, Australia
Fariza Sabrina: School of Engineering and Technology, Central Queensland University, Sydney, NSW 2000, Australia
Zongwen Fan: College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
Shaleeza Sohail: College of Engineering, Science and Environment, The University of Newcastle, Callaghan, NSW 2308, Australia

IJERPH, 2023, vol. 20, issue 15, 1-25

Abstract: Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client’s data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.

Keywords: federated learning; privacy enhancement; differential privacy; homomorphic encryption; blockchain; P2PS; edge device; edge federated learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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