Federated Learning for Privacy Preserving AI in Mental Health Applications

This paper looks at Federated Learning (FL) as a privacy-preserving method of deployment of AI within the domain of mental health. Although FL in healthcare, in general, is currently being viewed with some interest, extending FL to mental health-driven AI models is still a vast research lacuna. Mental health data is sensitive in nature and necessitates robust protection of privacy, which FL seemingly provides as a cost-efficient alternative to conventional centralized AI models. Our paper bridges this gap because we compare FL-based methods against centralized models along the dimensions of privacy, efficiency, and feasibility. In addition to privacy benefit assessment, we carry out a comparative analysis to identify the advantages and disadvantages of FL in mental health AI. We further suggest a more advanced FL framework that integrates block chain-based security protocols with adaptive aggregation techniques with the objectives of bridging weaknesses and enhancing robustness. The figures, tables, and experimental comparisons presented are empirical proof of our assertions, demonstrating improvements in privacy protection and model performance. We appreciate constructive criticism of our work and invite proposals for further improvement, further experiments, or other approaches. Through our contribution to the development of privacy-preserving AI in mental health treatment, we envision the ethical, safe, and effective implementation of AI-based mental health treatments. Our results hope to close the current research gap and act as a stepping stone for future research in this field.