Privacy-Preserving Mental Health Monitoring Using Federated Learning

Abstract

Mental health disorders are a major health issue everywhere and there is an increasing need for constant monitoring
systems that can help detect and intervene in early days of mental health issues, which are personalized.
With the recent advancements in Artificial Intelligence, mobile health, wearable sensors, and healthcare analytics,
intelligent mental health monitoring solutions have been created. But there are significant privacy, security and
ethical issues related to the gathering and processing of private psychological and behavioral data. Traditional
centralized machine learning (ML) systems typically involve sharing personal health data with central servers,
which introduces the risk of data breaches and unauthorized access.
In recent years, Federated learning has become an exciting new paradigm for machine learning that allows for
the collaborative training of a model across various devices and healthcare settings without sharing patient data.
Federated learning not only boosts data privacy but also aids in regulatory adherence and secured healthcare
analytics as it preserves data on locally owned devices, transmitting only model parameters. This paper will
discuss the architecture, privacy-preserving techniques, personalization capabilities, and integration with other
technologies like differential privacy, secure aggregation, blockchain, and edge computing of federated learning
in mental health monitoring systems. The study also assesses the advantages, obstacles, and implementation
issues of using federated learning for mental health assessments and predictions.
The results show that federated learning offers a promising way to achieve accurate mental health monitoring
without compromising user data sovereignty while maintaining a balance between predictive performance and
privacy protection. Although there are communication overhead, data heterogeneity, and security concerns,
the potential of federated learning provides a strong foundation for the creation of trustworthy and scalable
mental healthcare systems.