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.

利用联邦学习和上下文感知语言学习方法，FedTherapist提出了一种通过连续语音和键盘输入对移动设备进行隐私保护方式的移动心理健康监测系统，其通过比较多种模型设计的性能和开销，成功克服了在智能手机上进行设备内语言模型训练的复杂性，并通过对46名参与实验者进行自我报告的抑郁、压力、焦虑和情绪情况的预测的IRB批准评估显示出更高的准确性，相比非语言特征，AUROC改善了0.15，MAE降低了8.21%。

FedTherapist：通过联邦学习使用智能手机上的用户生成的语言表达进行心理健康监测