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
Journals
- Barker D, Tippireddy M, Farhan A, Ahmed B. Ethical Considerations in Emotion Recognition Research. Psychology International 2025;7(2):43 View
- 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
- 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
Books/Policy Documents
- Nath M, Tabdula M, Uppu N, Gajjala V, Dash S. Data Science and Applications. View
Conference Proceedings
- 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