Asynchronous peer-to-peer data mining with stochastic gradient descent

Róbert Ormándi, István Hegedus, Márk Jelasity

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

16 Citations (Scopus)

Abstract

Fully distributed data mining algorithms build global models over large amounts of data distributed over a large number of peers in a network, without moving the data itself. In the area of peer-to-peer (P2P) networks, such algorithms have various applications in P2P social networking, and also in trackerless BitTorrent communities. The difficulty of the problem involves realizing good quality models with an affordable communication complexity, while assuming as little as possible about the communication model. Here we describe a conceptually simple, yet powerful generic approach for designing efficient, fully distributed, asynchronous, local algorithms for learning models of fully distributed data. The key idea is that many models perform a random walk over the network while being gradually adjusted to fit the data they encounter, using a stochastic gradient descent search. We demonstrate our approach by implementing the support vector machine (SVM) method and by experimentally evaluating its performance in various failure scenarios over different benchmark datasets. Our algorithm scheme can implement a wide range of machine learning methods in an extremely robust manner.

Original languageEnglish
Title of host publicationEuro-Par 2011 Parallel Processing - 17th International Conference, Proceedings
Pages528-540
Number of pages13
EditionPART 1
DOIs
Publication statusPublished - Sep 8 2011
Event17th International Conference on Parallel Processing, Euro-Par 2011 - Bordeaux, France
Duration: Aug 29 2011Sep 2 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6852 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Parallel Processing, Euro-Par 2011
CountryFrance
CityBordeaux
Period8/29/119/2/11

    Fingerprint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Ormándi, R., Hegedus, I., & Jelasity, M. (2011). Asynchronous peer-to-peer data mining with stochastic gradient descent. In Euro-Par 2011 Parallel Processing - 17th International Conference, Proceedings (PART 1 ed., pp. 528-540). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6852 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-23400-2_49