A Privacy-Preserving Framework for Mental Health Prediction Using Federated Learning

The early detection of mental health disorders, particularly depression and anxiety, is essential for timely intervention and improved patient outcomes. Recent advances in Artificial Intelligence (AI) have enabled the development of systems capable of identifying psychological distress through multimodal data, including text, audio, and facial expressions. However, conventional AI models typically rely on centralized data collection, which poses significant risks to user privacy and data security, especially in healthcare applications involving sensitive personal information. This paper proposes a privacy-preserving framework for mental health prediction based on Federated Learning (FL). This decentralized training approach eliminates the need to transmit raw data to central servers. The proposed architecture integrates additional privacy-enhancing techniques such as differential privacy and secure aggregation to further safeguard user information. By enabling collaborative learning across distributed devices while maintaining data confidentiality, this framework offers a scalable and ethical solution for building AI systems in mental health care. The paper presents the system architecture, implementation strategy, and potential for real-world deployment