Intelligent depression detection with asynchronous federated optimization
Abstract The growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2022-06-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-022-00729-2 |
| _version_ | 1797863224744345600 |
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| author | Jinli Li Ming Jiang Yunbai Qin Ran Zhang Sai Ho Ling |
| author_facet | Jinli Li Ming Jiang Yunbai Qin Ran Zhang Sai Ho Ling |
| author_sort | Jinli Li |
| collection | DOAJ |
| description | Abstract The growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users. |
| first_indexed | 2024-04-09T22:32:08Z |
| format | Article |
| id | doaj.art-cdc0e699a8974dbea59ff567c07e9b83 |
| institution | Directory Open Access Journal |
| issn | 2199-4536 2198-6053 |
| language | English |
| last_indexed | 2024-04-09T22:32:08Z |
| publishDate | 2022-06-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj.art-cdc0e699a8974dbea59ff567c07e9b832023-03-22T12:43:43ZengSpringerComplex & Intelligent Systems2199-45362198-60532022-06-019111513110.1007/s40747-022-00729-2Intelligent depression detection with asynchronous federated optimizationJinli Li0Ming Jiang1Yunbai Qin2Ran Zhang3Sai Ho Ling4College of Electronic Engineering, Guangxi Normal UniversityCollege of Electronic Engineering, Guangxi Normal UniversityCollege of Electronic Engineering, Guangxi Normal UniversityBusiness and Law School, Deakin UniversityFaculty of Engineering and IT, University of TechnologyAbstract The growth of population and the various intensive life pressures everyday deepen the competitions among people. Tens of millions of people each year suffer from depression and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional vent window. People communicate with their friends, sharing their opinions, and shooting videos to reflect their feelings. It provides an opportunity to detect depression in social networks. Although depression detection using social networks has reflected the established connectivity across users, fewer researchers consider the data security and privacy-preserving schemes. Therefore, we advocate the federated learning technique as an efficient and scalable method, where it enables the handling of a massive number of edge devices in parallel. In this study, we conduct the depression analysis on the basis of an online microblog called Weibo. A novel algorithm termed as CNN Asynchronous Federated optimization (CAFed) is proposed based on federated learning to improve the communication cost and convergence rate. It is shown that our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. The proposed method converges faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques can identify quality solutions of mental health problems among Weibo users.https://doi.org/10.1007/s40747-022-00729-2Depression detectionAsynchronous federated learningCNN |
| spellingShingle | Jinli Li Ming Jiang Yunbai Qin Ran Zhang Sai Ho Ling Intelligent depression detection with asynchronous federated optimization Complex & Intelligent Systems Depression detection Asynchronous federated learning CNN |
| title | Intelligent depression detection with asynchronous federated optimization |
| title_full | Intelligent depression detection with asynchronous federated optimization |
| title_fullStr | Intelligent depression detection with asynchronous federated optimization |
| title_full_unstemmed | Intelligent depression detection with asynchronous federated optimization |
| title_short | Intelligent depression detection with asynchronous federated optimization |
| title_sort | intelligent depression detection with asynchronous federated optimization |
| topic | Depression detection Asynchronous federated learning CNN |
| url | https://doi.org/10.1007/s40747-022-00729-2 |
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