Privacy Preserving Language Models Detect Early Warning Signs in Digital Mental Health

Xiaoyu Deng

doi:10.54097/d4bfqr62

Authors

Xiaoyu Deng

DOI:

https://doi.org/10.54097/d4bfqr62

Keywords:

Mental health detection, Federated learning, Differential privacy, Natural language processing, Early warning signs, Language models, Digital psychiatry

Abstract

The proliferation of digital communication platforms has generated large-scale behavioral and linguistic data streams that carry meaningful signals about users' mental health trajectories. Deploying language models to analyze such data, however, introduces serious privacy challenges, as mental health-related text constitutes among the most sensitive categories of personal information. This paper proposes an integrated framework combining federated learning and differential privacy mechanisms to enable privacy-preserving language model training for early warning sign detection across depression, anxiety, and suicidal ideation. The architecture allows gradient updates to be computed locally on distributed client nodes and aggregated without centralizing raw user content, while formal DP guarantees bound the statistical influence of any individual's data on the released model. Experimental evaluations demonstrate competitive detection performance, with an area under the receiver operating characteristic curve of 0.847 at epsilon equal to five, approaching the non-private centralized baseline of 0.891. The results confirm that privacy-preserving approaches are technically feasible and clinically viable for real-world digital mental health monitoring applications.

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