Authors:
Aslam Jlassi
1
;
Afef Mdhaffar
1
;
2
;
Mohamed Jmaiel
1
;
2
and
Bernd Freisleben
3
Affiliations:
1
ReDCAD Laboratory, ENIS, University of Sfax, Sfax, Tunisia
;
2
Digital Research Center of Sfax, 3021, Sfax, Tunisia
;
3
Department of Mathematics and Computer Science, University of Marburg, 35032, Marburg, Germany
Keyword(s):
Federated Learning, Knowledge Distillation, Depression, Call Recording, Self-Distillation.
Abstract:
Depression affects over 280 million people globally and requires timely, accurate intervention to mitigate its effects. Traditional diagnostic methods often introduce delays and privacy concerns due to centralized data processing and subjective evaluations. To address these challenges, we propose a smartphone-based approach that uses federated learning to detect depressive episodes through the analysis of spontaneous phone calls. Our proposal protects user privacy by retaining data locally on user devices (i.e., smartphones). Our approach addresses catastrophic forgetting through the use of knowledge distillation, enabling efficient storage and robust learning. The experimental results demonstrate reasonable accuracy with minimal resource consumption, highlighting the potential of privacy-preserving AI solutions for mental health monitoring.