Gossip-based learning under drifting concepts in fully distributed networks

Istvan Hegedus, Robert Ormandi, Mark Jelasity

Research output: Conference contribution

7 Citations (Scopus)

Abstract

In fully distributed networks data mining is an important tool for monitoring, control, and for offering personalized services to users. The underlying data model can change as a function of time according to periodic (daily, weakly) patterns, sudden changes, or long term transformations of the environment or the system itself. For a large space of the possible models for this dynamism-when the network is very large but only a few training samples can be obtained at all nodes locally-no efficient fully distributed solution is known. Here we present an approach, that is able to follow concept drift in very large scale and fully distributed networks. The algorithm does not collect data to a central location, instead it is based on online learners taking random walks in the network. To achieve adaptivity the diversity of the learners is controlled by managing the life spans of the models. We demonstrate through a thorough experimental analysis, that in a well specified range of feasible models of concept drift, where there is little data available locally in a large network, our algorithm outperforms known methods from related work.

Original languageEnglish
Title of host publicationProceedings - 2012 IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2012
Pages79-88
Number of pages10
DOIs
Publication statusPublished - dec. 1 2012
Event2012 IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2012 - Lyon, France
Duration: szept. 10 2012szept. 14 2012

Publication series

NameInternational Conference on Self-Adaptive and Self-Organizing Systems, SASO
ISSN (Print)1949-3673
ISSN (Electronic)1949-3681

Other

Other2012 IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2012
CountryFrance
CityLyon
Period9/10/129/14/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Control and Systems Engineering

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  • Cite this

    Hegedus, I., Ormandi, R., & Jelasity, M. (2012). Gossip-based learning under drifting concepts in fully distributed networks. In Proceedings - 2012 IEEE 6th International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2012 (pp. 79-88). [6394113] (International Conference on Self-Adaptive and Self-Organizing Systems, SASO). https://doi.org/10.1109/SASO.2012.13