Privacy Preserved Brain Disorder Diagnosis Using Federated Learning.

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson's. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based approach embedded in a federated learning framework to detect and diagnose brain disorders. We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients, each with different data. To lessen the over-fitting aspect, every client reviewed the outcomes in three rounds. The proposed model identifies brain disorders without jeopardizing privacy and security. The results reveal that the global model achieves an accuracy of 82.82% for detecting brain disorders while preserving privacy. [ABSTRACT FROM AUTHOR]