# Differential Private Federated Transfer Learning for Mental Health Monitoring in Everyday Settings: A Case Study on Stress Detection

arXiv: https://arxiv.org/abs/2402.10862 · HTML: https://mlsanity.com/paper/2402.10862/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 16 Feb 2024.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://mlsanity.com/paper/1812.00910/) (2018)
- [DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization](https://mlsanity.com/paper/2010.00573/) (2020)
- [SAIC: Integration of Speech Anonymization and Identity Classification](https://mlsanity.com/paper/2312.15190/) (2023)
- [ZotCare: A Flexible, Personalizable, and Affordable mHealth Service Provider](https://mlsanity.com/paper/2307.01905/) (2023)
- [Edge-centric Optimization of Multi-modal ML-driven eHealth Applications](https://mlsanity.com/paper/2208.02597/) (2022)
- [Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings](https://mlsanity.com/paper/2305.00111/) (2023)

## Built on by (most cited first, 16 of 16)

- [HealthQ: Unveiling Questioning Capabilities of LLM Chains in Healthcare Conversations](https://mlsanity.com/paper/2409.19487/) (2024)
- [Towards Foundation-model-based Multiagent System to Accelerate AI for Social Impact](https://mlsanity.com/paper/2412.07880/) (2024)
- [Integrating Wearable Sensor Data and Self-reported Diaries for Personalized Affect Forecasting](https://mlsanity.com/paper/2403.13841/) (2024)
- [Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach](https://mlsanity.com/paper/2407.08215/) (2024)
- [ECG Unveiled: Analysis of Client Re-identification Risks in Real-World ECG Datasets](https://mlsanity.com/paper/2408.10228/) (2024)
- [Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications](https://mlsanity.com/paper/2506.00660/) (2025)
- [Efflex: Efficient and Flexible Pipeline for Spatio-Temporal Trajectory Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.12400/) (2024)
- [Linkage Attacks Expose Identity Risks in Public ECG Data Sharing](https://mlsanity.com/paper/2508.15850/) (2025)
- [VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning](https://mlsanity.com/paper/2404.08021/) (2024)
- [TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis](https://mlsanity.com/paper/2503.13495/) (2025)
- [CrossGP: Cross-Day Glucose Prediction Excluding Physiological Information](https://mlsanity.com/paper/2404.10901/) (2024)
- [GluMarker: A Novel Predictive Modeling of Glycemic Control Through Digital Biomarkers](https://mlsanity.com/paper/2404.12605/) (2024)
- [Toward Short-Term Glucose Prediction Solely Based on CGM Time Series](https://mlsanity.com/paper/2404.11924/) (2024)
- [Membership Inference Attacks Expose Participation Privacy in ECG Foundation Encoders](https://mlsanity.com/paper/2604.10424/) (2026)
- [PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning](https://mlsanity.com/paper/2605.07267/) (2026)
- [FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data](https://mlsanity.com/paper/2605.18936/) (2026)

## Abstract (arXiv)

Mental health conditions, prevalent across various demographics, necessitate efficient monitoring to mitigate their adverse impacts on life quality. The surge in data-driven methodologies for mental health monitoring has underscored the importance of privacy-preserving techniques in handling sensitive health data. Despite strides in federated learning for mental health monitoring, existing approaches struggle with vulnerabilities to certain cyber-attacks and data insufficiency in real-world applications. In this paper, we introduce a differential private federated transfer learning framework for mental health monitoring to enhance data privacy and enrich data sufficiency. To accomplish this, we integrate federated learning with two pivotal elements: (1) differential privacy, achieved by introducing noise into the updates, and (2) transfer learning, employing a pre-trained universal model to adeptly address issues of data imbalance and insufficiency. We evaluate the framework by a case study on stress detection, employing a dataset of physiological and contextual data from a longitudinal study. Our finding show that the proposed approach can attain a 10% boost in accuracy and a 21% enhancement in recall, while ensuring privacy protection.