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

2024-10-17

arXiv.org

AI Summary

This work addresses privacy and resource constraints in smartphone-based textual depression detection by proposing the first on-device federated learning (FL) framework for decentralized depression symptom identification using Reddit posts. To reduce edge computational overhead, we design a cross-device unified tokenizer and integrate lightweight NLP preprocessing with GRU/LSTM networks to optimize training efficiency across heterogeneous mobile clients. Experiments conducted in real smartphone environments demonstrate that our FL model achieves performance comparable to centralized training while significantly reducing communication and memory overhead, strictly ensuring raw data remains local. Key contributions include: (1) the first systematic application of FL to mobile depression detection; (2) a low-overhead unified tokenization mechanism enabling consistent text representation across diverse devices; and (3) empirical validation of FL’s feasibility, privacy compliance, and resource efficiency in realistic mobile settings.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Detect depression from Reddit posts using federated learning
• Protect user data privacy in decentralized smartphone training
• Evaluate neural network performance in federated learning settings

Innovation

• Federated Learning for decentralized smartphone training
• Common tokenizer reduces computational load
• Analyze resource consumption in FL environment