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 

机构地区：[1]Institute of Informatics and Communication,University of Delhi,Delhi,110021,India [2]Maharaja Agrasen College,University of Delhi,Delhi,110096,India [3]Computer Engineering,Ronin Institute,Montclair,NJ 07043,USA [4]Department of Research and Innovation,Insights2Techinfo,Jaipur,302001,India [5]University Centre for Research and Development(UCRD),Chandigarh University,Chandigarh,140413,India [6]Management Department,College of Business Administration,Princess Nourah bint Abdulrahman University,Riyadh,11671,Saudi Arabia [7]CCRI&Department of Computer Science and Information Engineering,Asia University,Taichung,413,Taiwan [8]Symbiosis Centre for Information Technology(SCIT),Symbiosis International University,Pune,411057,India [9]Center for Interdisciplinary Research,University of Petroleum and Energy Studies(UPES),Dehradun,248007,India

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期49-90,共42页工程与科学中的计算机建模（英文）

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

摘　　要：Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It 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 applications.This 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 concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关 键 词：DEPRESSION emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] B84[自动化与计算机技术—计算机科学与技术]