Federated Learning for Private AI Diagnosis of Schizophrenia

Abstract

This study delves into the realm of federated learning, focusing on its application in the private and accurate artificial intelligence (AI) diagnosis of schizophrenia. Leveraging the collaborative power of distributed datasets without compromising individual privacy, the research investigates the feasibility and effectiveness of federated learning models. The study employs advanced AI algorithms for schizophrenia diagnosis, ensuring the confidentiality of patient data. The results demonstrate the potential of federated learning as a secure and efficient approach for enhancing diagnostic capabilities in mental health, specifically in the context of schizophrenia. This research contributes to the ongoing efforts to harness cutting-edge technologies for improved mental health diagnostics while prioritizing individual privacy.

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