JMIR Publications

ABSTRACT

Background:

Advances in sensing and other communication technologies, as well as artificial intelligence (AI), have driven the widespread use of mobile health (mHealth) applications. For example, data collected from sensor devices carried by patients can be mined and analyzed using AI-based solutions to facilitate remote and (near) real-time decision-making in healthcare settings. However, patients may not wish to share their raw data due to privacy concerns. One possible solution is to utilize federated learning (FL) where only the trained parameters (and not the raw data) are shared during the model training process.

Objective:

This scoping literature review explains what federated learning is, and how it can be used to deal with sensitive and heterogeneous data in mHealth applications so that it can help healthcare practitioners understand the associated limitations and challenges of FL in mHealth.

Methods:

A scoping review was conducted following the guidelines of PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) [1]. We searched seven (7) commonly used databases. The included studies were analyzed and summarized to identify possible real-world applications and associated challenges of applying FL in healthcare settings. A total of 1,095 articles were retrieved during the database search, and 26 articles that met the inclusion criteria were eventually included in the review.

Results:

Common applications of FL in mHealth include monitoring self-care ability, monitoring mental health status, disease progression, and auxiliary diagnosis of disease. Based on the analysis, we identified a number of research challenges and opportunities, such as those relating to communication costs, statistical heterogeneity, and privacy leakage.

Conclusions:

While FL is a viable approach to addressing privacy concerns in mHealth applications, there are a number of technical limitations associated with the use of FL as outlined in this article. Hopefully, the challenges and opportunities identified in this work will inform future research agenda on this topic.