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Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM

The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We in...

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Detalles Bibliográficos
Autores principales:	Tavara, Shirin, Schliep, Alexander
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	PeerJ Inc. 2021
Materias:	Data Mining and Machine Learning
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959654/ https://www.ncbi.nlm.nih.gov/pubmed/33817043 http://dx.doi.org/10.7717/peerj-cs.397

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