On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts

TL;DR

This paper explores federated learning on smartphones to detect depression from Reddit posts, maintaining privacy while achieving performance comparable to centralized models, and analyzing resource and communication costs.

Contribution

It introduces a federated learning approach for depression detection on edge devices, optimizing training with shared tokenization and evaluating resource efficiency.

Findings

Federated models perform comparably to centralized models.

Shared tokenizer reduces computational load on smartphones.

Resource and communication costs are manageable in real-world settings.

Abstract

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.