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

Area of Science:

• Computational linguistics
• Mental health informatics
• Machine learning for healthcare

Background:

• Rising rates of mental health conditions like depression necessitate early detection strategies.
• Analyzing patient text data with natural language processing (NLP) shows promise but faces privacy hurdles.
• Federated learning (FL) offers a solution by enabling model training without centralizing sensitive patient data.

Purpose of the Study:

• To evaluate the efficacy of federated learning (FL) models for depression detection using multilingual social media data.
• To assess FL performance under varying data conditions, including different languages and sample sizes.
• To verify FL's capability in preserving client privacy during the model training process.

Main Methods:

• Utilized a simulated multilingual dataset comprising social media posts in five languages.
• Applied federated learning (FL) techniques for training depression detection models.
• Analyzed model performance across different client partitioning strategies (independent and non-independent).

Main Results:

• Federated learning (FL) demonstrated strong performance in depression detection across most tested scenarios.
• FL effectively maintained client privacy, a critical factor for sensitive health data.
• Performance remained robust even with variations in language and sample size across clients.

Conclusions:

• Federated learning (FL) is a viable and effective method for privacy-preserving mental health analysis using textual data.
• FL successfully balances the need for robust model performance with the critical requirement of data privacy.
• This approach holds significant potential for advancing early detection of mental health conditions like depression.