  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.

Computation and Language;Artificial Intelligence;Machine Learning