# FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data

arXiv: https://arxiv.org/abs/2605.18936 · HTML: https://mlsanity.com/paper/2605.18936/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Differentially Private Learning Needs Better Features (or Much More Data)](https://mlsanity.com/paper/2011.11660/) (2020)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Benchmarking Differential Privacy and Federated Learning for BERT Models](https://mlsanity.com/paper/2106.13973/) (2021)
- [Multi-Task Learning for Mental Health using Social Media Text](https://mlsanity.com/paper/1712.03538/) (2017)
- [Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health](https://mlsanity.com/paper/2304.10447/) (2023)
- [Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection](https://mlsanity.com/paper/2402.10862/) (2024)
- [Federated and Differentially Private Learning for Electronic Health Records](https://mlsanity.com/paper/1911.05861/) (2019)
- [Diverse Perspectives, Divergent Models: Cross-Cultural Evaluation of Depression Detection on Twitter](https://mlsanity.com/paper/2406.15362/) (2024)
- [Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features](https://mlsanity.com/paper/2003.04763/) (2020)
- [A Comprehensive Survey On Client Selections in Federated Learning](https://mlsanity.com/paper/2311.06801/) (2023)
- [A Federated Approach to Predicting Emojis in Hindi Tweets](https://mlsanity.com/paper/2211.06401/) (2022)
- [FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health](https://mlsanity.com/paper/2509.14275/) (2025)
- [Advancing a Consent-Forward Paradigm for Digital Mental Health Data](https://mlsanity.com/paper/2404.14548/) (2024)

## Abstract (arXiv)

Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance. Specifically, we apply federated learning (FL) and Differentially Private FL for two widely-studied mental health prediction tasks: depression detection on X (Twitter) and suicide crisis detection on Reddit. We simulate realistic data-sharing scenarios by treating each user as a client in a non-IID setting, evaluating across different client fractions, aggregation strategies, and privacy budgets. While FL achieves comparable performance to centralized training (centralized F1 = 85.63; best FL model F1 = 83.16) on depression identification, we find that Differentially Private FL has a large performance-privacy trade-off (up to F1 = 27.01 drop) even with low levels of noise (epsilon = 50). This is due to the distortion of highly informative yet sparse mental health linguistic markers related to mental health, like health topics and emotion words. This research empirically demonstrates the potential and limitations of current privacy preservation techniques for mental health inference tasks.