Online Supervised Learning with Distributed Features over Multiagent System
Joint Authors
Hu, Chen
An, Xibin
He, Bing
Liu, Bingqi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-16
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Most current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks.
We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to collaborate but do not exchange the raw data with each other.
We propose a distributed feature online gradient descent algorithm and prove that local solution converges to the global minimizer with a sublinear rate O2T.
Our algorithm does not require exchange of the primal data or even the model parameters between agents.
Firstly, we design an auxiliary variable, which implies the information of the global features, and estimate at each agent by dynamic consensus method.
Then, local parameters are updated by online gradient descent method based on local data stream.
Simulations illustrate the performance of the proposed algorithm.
American Psychological Association (APA)
An, Xibin& He, Bing& Hu, Chen& Liu, Bingqi. 2020. Online Supervised Learning with Distributed Features over Multiagent System. Complexity،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144739
Modern Language Association (MLA)
An, Xibin…[et al.]. Online Supervised Learning with Distributed Features over Multiagent System. Complexity No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1144739
American Medical Association (AMA)
An, Xibin& He, Bing& Hu, Chen& Liu, Bingqi. Online Supervised Learning with Distributed Features over Multiagent System. Complexity. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1144739
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1144739