Depression Detection Using Asynchronous Federated Optimization
Author:
Affiliation:
1. College of Electronics and Engineering, Guangxi Normal University,China
2. School of Business and Law, Deakin University,Melbourne,Australia
3. Bevercapital and BCU world Pty Ltd
Funder
National Natural Science Foundation of China
Natural Science Foundation of Guangxi
Guangxi Key Lab of Multi-source Information Mining & Security
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9724288/9724267/09724416.pdf?arnumber=9724416
Reference27 articles.
1. Federated Optimization: Distributed Machine Learning for On-Device Intelligence;kone?ný;ArXiv,0
2. A text classification framework for simple and effective early depression detection over social media streams
3. Federated Machine Learning
4. Federated learning for 6G communications: Challenges, methods, and future directions
5. Asynchronous Federated Optimization;xie;ArXiv,0
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3. Wireless Federated Learning With Asynchronous and Quantized Updates;IEEE Communications Letters;2023-09
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