Privacy Preserved Brain Disorder Diagnosis Using Federated Learning  

作　　者：Ali Altalbe Abdul Rehman Javed 

机构地区：[1]Department of Computer Science,Prince Sattam Bin Abdulaziz University,Al-Kharj,11942,Saudi Arabia [2]Faculty of Computing and Information Technology,King Abdulaziz University,Jeddah,21589,Saudi Arabia [3]Department of Electrical and Computer Engineering,Lebanese American University,Byblos,Lebanon

出　　处：《Computer Systems Science & Engineering》2023年第11期2187-2200,共14页计算机系统科学与工程（英文）

基　　金：supported by the Deanship of Scientific Research at Prince Sattam bin Aziz University under the Research Project (PSAU/2023/01/22425).

摘　　要：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.

关 键 词：Privacy preservation brain disorder detection Parkinson’s disease diagnosis federated learning healthcare machine learning 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]