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SYSTEMATIC REVIEW article

Front. Digit. Health
Sec. Digital Mental Health
Volume 6 - 2024 | doi: 10.3389/fdgth.2024.1495999
This article is part of the Research Topic Cybersecurity in Digital Mental Health View all 4 articles

A systematic survey on the application of federated learning in mental state detection and human activity recognition

Provisionally accepted
Albin Grataloup Albin Grataloup *Mascha Kurpicz-Briki Mascha Kurpicz-Briki
  • Bern University of Applied Sciences, Bern, Switzerland

The final, formatted version of the article will be published soon.

    This systematic review investigates the application of federated learning in mental health and human activity recognition. A comprehensive search was conducted to identify studies utilizing federated learning for these domains. The included studies were evaluated based on publication year, task, dataset characteristics, federated learning algorithms, and personalization methods.The aim is to provide an overview of the current state-of-the-art, identify research gaps, and inform future research directions in this emerging field.

    Keywords: Mental Health, Well-being, Human activity detection, Federated learning, Data heterogeneity, Personalization, distributed, Privacy-preserving

    Received: 13 Sep 2024; Accepted: 14 Nov 2024.

    Copyright: © 2024 Grataloup and Kurpicz-Briki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Albin Grataloup, Bern University of Applied Sciences, Bern, Switzerland

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.