Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM
Journal article, 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.

Distributed optimization

Convergence

ADMM

SVMs

Expander graphs

Parallel and distributed computing

Machine learning

Author

Shirin Tavara

Chalmers, Computer Science and Engineering (Chalmers), Data Science

Alexander Schliep

University of Gothenburg

PeerJ Computer Science

23765992 (eISSN)

Vol. 7 e397

Subject Categories

Telecommunications

Computer Science

Computer Systems

DOI

10.7717/peerj-cs.397

PubMed

33817043

More information

Latest update

3/21/2023