This paper is in the following e-collection/theme issue:

Diagnostic Tools in Mental Health (327) Affective Computing (9) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2121) Transdiagnostic Mental Health Interventions (208) Wearable Devices and Sensors (1141) Ethics, Privacy, and Legal Issues (621) Security and Privacy of mHealth and uHealth (159) Mobile Health (mhealth) (3476)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60003, first published .
Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study

Balancing Between Privacy and Utility for Affect Recognition Using Multitask Learning in Differential Privacy–Added Federated Learning Settings: Quantitative Study

Authors of this article:

Mohamed Benouis1 Author Orcid Image ;   Elisabeth Andre1 Author Orcid Image ;   Yekta Said Can1 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Barker D, Tippireddy M, Farhan A, Ahmed B. Ethical Considerations in Emotion Recognition Research. Psychology International 2025;7(2):43 View
  2. Shehada D, Tawfik H, Bouridane A, Hussain A. An Explainable Framework for Mental Health Monitoring Using Lightweight and Privacy-Preserving Federated Facial Emotion Recognition. Sensors 2025;25(23):7320 View