Accessibility settings

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

Diagnostic Tools in Mental Health (437) Ethics, Privacy, and Legal Issues (807) Mobile Health (mhealth) (5157) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (3200) Security and Privacy of mHealth and uHealth (216) Wearable Devices and Sensors (1577) Transdiagnostic Mental Health Interventions (270) Affective Computing (36)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/60003, first published .
Digital security concept: person using laptop, phone, and digital interface showing profile and password lock.

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 (13) 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
  3. Schefzik R, Cao H, Rajan S, Escribà-Montagut X, González J, Schwarz E, Zhu S. Integrating differential privacy into federated multi-task learning algorithms in dsMTL. Bioinformatics Advances 2024;5(1) View
  4. Lima V, Martinho D. Biomimetic Synthetic Somatic Markers in the Pixelverse: A Bio-Inspired Framework for Intuitive Artificial Intelligence. Biomimetics 2026;11(1):63 View
  5. Benouis M, Andre E, Can Y. Leveraging Federated Self-Supervised Approach for Stress Recognition to Mitigate Label Leakage. ACM Transactions on Computing for Healthcare 2026 View
  6. Nikookar N, Sinha A, Deshpande S, Ayati P, Sawyer A, Eslami M, Goel M, Rockcastle S. Affective lighting in built environments: A scoping review of evidence, technologies, and future directions. Building and Environment 2026;295:114446 View
  7. Dolezel D, Lalani K, Watzlaf V, Butler-Henderson K, Lambert E, Morton M, Sand J, Gibbs D, Fenton S. AI Privacy and Security in Healthcare: A Systematic Literature Review. International Journal of Telerehabilitation 2026;18(1) View
  8. van Zyl L. Positive psychology 4.0: a science of consequence for a world that can no longer wait. Frontiers in Psychology 2026;17 View
  9. Fang L, Li J, Fang C, Chen X, Zhu Y, Xu Y, Jia Wang S. Beyond accuracy: user perception of differential privacy in speech emotion recognition. Interacting with Computers 2026 View
  10. Shehada D, Tawfik H, Bouridane A, Hussain A. Trustworthy and explainable lightweight facial emotion recognition for mental health monitoring. Informatics in Medicine Unlocked 2026;65:101788 View

Books/Policy Documents

  1. Nath M, Tabdula M, Uppu N, Gajjala V, Dash S. Data Science and Applications. View

Conference Proceedings

  1. Hadke P, Patil S. 2025 International Conference on Sustainable Communication Networks and Application (ICSCN). Federated Learning Architectures for Mental Health Data: A Systematic Review of Privacy-Preserving Approaches and Clinical Efficacy View
  2. Truong N, Hickling A, Rahman R, Flanigen J, Huang P, Kastelic J, Truex B, Parthemore G, Pilewicz J, Gooneratne P, Domingo P, Samarawickrama M. 2026 IEEE International Conference on AI and Data Analytics (ICAD). Adoption of Safe and Responsible AI for Streamlining Communication with Verbally Impaired Communities View