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

arXiv: https://arxiv.org/abs/2410.13709 · HTML: https://mlsanity.com/paper/2410.13709/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 17 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [FedFed: Feature Distillation against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2310.05077/) (2023)
- [Differential Privacy-enabled Federated Learning for Sensitive Health Data](https://mlsanity.com/paper/1910.02578/) (2019)
- [Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision](https://mlsanity.com/paper/2303.00885/) (2023)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [FedMood: Federated Learning on Mobile Health Data for Mood Detection](https://mlsanity.com/paper/2102.09342/) (2021)
- [Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression](https://mlsanity.com/paper/2205.00111/) (2022)
- [FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps](https://mlsanity.com/paper/2111.09445/) (2021)
- [On the Efficiency of Privacy Attacks in Federated Learning](https://mlsanity.com/paper/2404.09430/) (2024)
- [KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text](https://mlsanity.com/paper/2204.04481/) (2022)
- [FLaaS: Cross-App On-device Federated Learning in Mobile Environments](https://mlsanity.com/paper/2206.10963/) (2022)

## Built on by (most cited first, 2 of 2)

- [Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2](https://mlsanity.com/paper/2505.21715/) (2025)
- [FedRP: A Communication-Efficient Approach for Differentially Private Federated Learning Using Random Projection](https://mlsanity.com/paper/2509.10041/) (2025)

## Abstract (arXiv)

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.