Federated learning for privacy-preserving depression detection with multilingual language models in social media posts

Patterns, vol. 5, pp. 100990

Abstract

The incidences of mental health illnesses, such as suicidal ideation and depression, are increasing, which highlights the urgent need for early detection methods. There is a growing interest in using natural language processing (NLP) models to analyze textual data from patients, but accessing patients' data for research purposes can be challenging due to privacy concerns. Federated learning (FL) is a promising approach that can balance the need for centralized learning with data ownership sensitivity. In this study, we examine the effectiveness of FL models in detecting depression by using a simulated multilingual dataset. We analyzed social media posts in five different languages with varying sample sizes. Our findings indicate that FL achieves strong performance in most cases while maintaining clients' privacy for both independent and non-independent client partitioning.

Authors 3

1. Samar Samir Khalil corresponding Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Arab Academy for Science, Technology, and Maritime Transport

   Affiliation as printed

   Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

   Leiden Institute of Advanced Computer Science, Leiden University, 2333 CA Leiden, the Netherlands

2. Noha S. Tawfik corresponding

   Arab Academy for Science, Technology, and Maritime Transport

   Affiliation as printed

   Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

3. Marco René Spruit Aachen Leiden Institute of Advanced Computer Science Public Health and Primary Care

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Leiden Institute of Advanced Computer Science, Leiden University, 2333 CA Leiden, the Netherlands

   Public Health & Primary Care, Leiden University Medical Center, 2333 CA Leiden, the Netherlands