Depression detection using deep learning models has been widely explored in previous studies, especially due to the large amounts of data available from social media posts. These posts provide valuable information about individuals' mental health conditions and can be leveraged to train models and identify patterns in the data. However, distributed learning approaches have not been extensively explored in this domain. In this study, we adopt Federated Learning (FL) to facilitate decentralized training on smartphones while protecting user data privacy. We train three neural network architectures--GRU, RNN, and LSTM on Reddit posts to detect signs of depression and evaluate their performance under heterogeneous FL settings. To optimize the training process, we leverage a common tokenizer across all client devices, which reduces the computational load. Additionally, we analyze resource consumption and communication costs on smartphones to assess their impact in a real-world FL environment. Our experimental results demonstrate that the federated models achieve comparable performance to the centralized models. This study highlights the potential of FL for decentralized mental health prediction by providing a secure and efficient model training process on edge devices.

翻译：利用深度学习模型进行抑郁症检测在先前研究中已得到广泛探索，这尤其得益于社交媒体帖子中可获取的大量数据。这些帖子提供了有关个体心理健康状况的宝贵信息，可用于训练模型并识别数据中的模式。然而，分布式学习方法在该领域尚未得到深入探索。在本研究中，我们采用联邦学习（FL）以促进智能手机上的去中心化训练，同时保护用户数据隐私。我们在Reddit帖子上训练了三种神经网络架构——GRU、RNN和LSTM，以检测抑郁迹象，并在异构FL设置下评估其性能。为优化训练过程，我们在所有客户端设备上采用通用分词器，从而降低了计算负载。此外，我们分析了智能手机上的资源消耗与通信成本，以评估其在真实世界FL环境中的影响。实验结果表明，联邦模型达到了与集中式模型相当的性能。本研究通过为边缘设备提供安全高效的模型训练过程，凸显了FL在去中心化心理健康预测方面的潜力。