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.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.