# The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions

arXiv: https://arxiv.org/abs/2501.11714 · HTML: https://mlsanity.com/paper/2501.11714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 196 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 28 of 28)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Machine Unlearning](https://mlsanity.com/paper/1912.03817/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [A Survey on Homomorphic Encryption Schemes: Theory and Implementation](https://mlsanity.com/paper/1704.03578/) (2017)
- [FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning](https://mlsanity.com/paper/2309.00363/) (2023)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Federated Unlearning: How to Efficiently Erase a Client in FL?](https://mlsanity.com/paper/2207.05521/) (2022)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks](https://mlsanity.com/paper/2007.09511/) (2020)
- [Fed2: Feature-Aligned Federated Learning](https://mlsanity.com/paper/2111.14248/) (2021)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [Forecasting the onset and course of mental illness with Twitter data](https://mlsanity.com/paper/1608.07740/) (2016)
- [Decentralized Federated Learning for UAV Networks: Architecture, Challenges, and Opportunities](https://mlsanity.com/paper/2104.07557/) (2021)
- [Heterogeneous Multi-task Learning with Expert Diversity](https://mlsanity.com/paper/2106.10595/) (2021)
- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Connectivity-Aware Semi-Decentralized Federated Learning over Time-Varying D2D Networks](https://mlsanity.com/paper/2303.08988/) (2023)
- [FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning](https://mlsanity.com/paper/2310.16538/) (2023)
- [Privacy Sensitive Speech Analysis Using Federated Learning to Assess Depression](https://mlsanity.com/paper/2205.00111/) (2022)
- [Federated Learning with Server Learning: Enhancing Performance for Non-IID Data](https://mlsanity.com/paper/2210.02614/) (2022)
- [Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities](https://mlsanity.com/paper/2401.03609/) (2024)
- [Using mobile phone sensor technology for mental health research: Integrated analysis to identify hidden challenges and potential solutions](https://mlsanity.com/paper/1805.09158/) (2018)

## Built on by (most cited first, 1 of 1)

- [Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges](https://mlsanity.com/paper/2509.07946/) (2025)

## Abstract (arXiv)

Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high school and college -- investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this paper, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.