Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
Artikel i vetenskaplig tidskrift, 2021

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.

Parallel and distributed computing

ADMM

SVMs

Machine learning

Distributed optimization

Convergence

Expander graphs

Författare

Shirin Tavara

Chalmers, Data- och informationsteknik, Data Science

PEERJ COMPUTER SCIENCE

2376-5992 (ISSN)

e397

Ämneskategorier

Telekommunikation

Datavetenskap (datalogi)

Datorsystem

DOI

10.7717/peerj-cs.397

PubMed

33817043

Mer information

Senast uppdaterat

2021-04-14