A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

作     者：Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta 

作者机构：Institute of Informatics and CommunicationUniversity of DelhiDelhi110021India Maharaja Agrasen CollegeUniversity of DelhiDelhi110096India Computer EngineeringRonin InstituteMontclairNJ 07043USA Department of Research and InnovationInsights2TechinfoJaipur302001India University Centre for Research and Development(UCRD)Chandigarh UniversityChandigarh140413India Management DepartmentCollege of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information EngineeringAsia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT)Symbiosis International UniversityPune411057India Center for Interdisciplinary ResearchUniversity of Petroleum and Energy Studies(UPES)Dehradun248007India 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2025年第142卷第1期

页      面：49-90页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R 343) Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia 

主　　题：Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour 

摘      要：Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.