mlsanityData to 14 Oct 2026

FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning

arXiv 2310.16538 ↗First posted 25 Oct 2023cs.CL cs.AI cs.LGcited by 10

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Abstract (arXiv)

Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features such as activity, app usage, and location via mobile devices. We propose FedTherapist, a mobile mental health monitoring system that utilizes continuous speech and keyboard input in a privacy-preserving way via federated learning. We explore multiple model designs by comparing their performance and overhead for FedTherapist to overcome the complex nature of on-device language model training on smartphones. We further propose a Context-Aware Language Learning (CALL) methodology to effectively utilize smartphones' large and noisy text for mental health signal sensing. Our IRB-approved evaluation of the prediction of self-reported depression, stress, anxiety, and mood from 46 participants shows higher accuracy of FedTherapist compared with the performance with non-language features, achieving 0.15 AUROC improvement and 8.21% MAE reduction.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 36 of its 100 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

36 of 100 references.

References · 36

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24 more
  1. 2015Semi-supervised Sequence Learningcited by 198
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 4; 2026: 3.

10 papers.

  1. 2024Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Securitycited by 260
  2. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  3. 2024Synergizing Foundation Models and Federated Learning: A Surveycited by 14
  4. 2025FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Healthcited by 5
  5. 2025Menta: A Small Language Model for On-Device Mental Health Predictioncited by 3
  6. 2026Responsible Evaluation of AI for Mental Healthcited by 2
  7. 2025The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directionscited by 1
  8. 2025HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregationcited by 0
  9. 2026Evaluating Federated Learning for Cross-Country Mood Inference from Smartphone Sensing Datacited by 0
  10. 2026Trust Stack for Mental Health AI: A Survey of Calibration across Human, Interaction, and AI Layerscited by 0