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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
PeerJ Inc.
2021
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| Materias: | |
| 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 |