Consensus-based distributed linear support vector machines

Pedro A. Forero, Alfonso Cano, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

This paper develops algorithms to train linear support vector machines (SVMs) when training data are distributed across different nodes and their communication to a centralized node is prohibited due to, for example, communication overhead or privacy reasons. To accomplish this goal, the centralized linear SVM problem is cast as the solution of coupled decentralized convex optimization subproblems with consensus constraints on the parameters defining the classifier. Using the method of multipliers, distributed training algorithms are derived that do not exchange elements from the training set among nodes. The communications overhead of the novel approach is fixed and fully determined by the topology of the network instead of being determined by the size of the training sets as it is the case for existing incremental approaches. An online algorithm where data arrive sequentially to the nodes is also developed. Simulated tests illustrate the performance of the algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10
Pages35-46
Number of pages12
DOIs
StatePublished - Jul 16 2010
Event9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010 - Stockholm, Sweden
Duration: Apr 12 2010Apr 16 2010

Publication series

NameProceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN '10

Other

Other9th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2010
CountrySweden
CityStockholm
Period4/12/104/16/10

Keywords

  • optimization
  • sensor networks
  • support vector machines

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