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

Tavara, Shirin ; Schliep, Alexander

PeerJ. Computer science, 2021-03, Vol.7, p.e397-e397, Article e397 [Periódico revisado por pares]

United States: PeerJ. Ltd

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Título:
Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
Autor: Tavara, Shirin ; Schliep, Alexander
Assuntos: ADMM ; alternating direction method ; Analysis ; Communication ; Computer and Information Science ; Computer Science ; Connectivity ; Convergence ; Data Mining and Machine Learning ; Data- och informationsvetenskap ; Distributed and Parallel Computing ; Distributed optimization ; Eigen values ; Expander graphs ; Graphs ; Machine learning ; Network architecture ; Network Science and Online Social Networks ; Network topologies ; Optimization ; Parallel and distributed computing ; Support vector machines ; SVMs
É parte de: PeerJ. Computer science, 2021-03, Vol.7, p.e397-e397, Article e397
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: 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 investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.
Editor: United States: PeerJ. Ltd
Idioma: Inglês

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

Tavara, Shirin ; Schliep, Alexander

United States: PeerJ. Ltd

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