A scoping review of the governance of federated learning in healthcare

Abstract

In healthcare, federated learning (FL) is emerging as a methodology to enable the analysis of large and disparate datasets while allowing custodians to retain sovereignty. While FL minimises data-sharing challenges, concerns surrounding ethics, privacy, maleficent use, and harm remain. These concerns can be managed by effective data governance. Data governance specifies procedural, relational, and structural mechanisms governing how data is captured, shared, and analysed, the resultant models and their use. However, limited insights exist on the optimal governance of this emerging technology. This study aims to develop a consolidated framework of the data governance mechanisms for FL in healthcare. A scoping review was performed, using deductive and inductive analysis of 39 articles. The framework includes twelve procedural, ten relational, and twelve structural mechanisms. The framework directs researchers to examine how to enact each mechanism and provides practitioners with insights into the mechanism to consider when governing FL.

Fig. 1. Article screening and selection process.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram detailing the article screening process.

Fig. 2. Procedural, relational, and structural mechanisms necessary to govern FL.

Note: asterisk (*) denotes that the mechanism has yet to be explicitly reported in FL papers but are apparent in FDN/ML papers. Future research is necessary to identify their relevance and how they can be tailored to FL.