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Intelligent depression detection with asynchronous federated optimization
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 pr...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217731/ https://www.ncbi.nlm.nih.gov/pubmed/35761865 http://dx.doi.org/10.1007/s40747-022-00729-2 |
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author | Li, Jinli Jiang, Ming Qin, Yunbai Zhang, Ran Ling, Sai Ho |
author_facet | Li, Jinli Jiang, Ming Qin, Yunbai Zhang, Ran Ling, Sai Ho |
author_sort | Li, Jinli |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9217731 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-92177312022-06-23 Intelligent depression detection with asynchronous federated optimization Li, Jinli Jiang, Ming Qin, Yunbai Zhang, Ran Ling, Sai Ho Complex Intell Systems Original Article 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. Springer International Publishing 2022-06-23 2023 /pmc/articles/PMC9217731/ /pubmed/35761865 http://dx.doi.org/10.1007/s40747-022-00729-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visithttp://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Li, Jinli Jiang, Ming Qin, Yunbai Zhang, Ran Ling, Sai Ho Intelligent depression detection with asynchronous federated optimization |
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 | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217731/ https://www.ncbi.nlm.nih.gov/pubmed/35761865 http://dx.doi.org/10.1007/s40747-022-00729-2 |
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