Byzantine Resilient Distributed Clustering with Redundant Data Assignment

Saikiran Bulusu, Venkata Gandikota, Arya Mazumdar, Ankit Singh Rawat, Pramod K. Varshney

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

Abstract

In this paper, we present robust variants of distributed clustering algorithms for large datasets distributed across multiple machines in the presence of Byzantines. We propose a redundant data assignment scheme that enables us to obtain global information about the entire dataset for clustering purposes even when some machines are adversarial in nature. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.

Original languageEnglish (US)
Title of host publication2021 IEEE International Symposium on Information Theory, ISIT 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2143-2148
Number of pages6
ISBN (Electronic)9781538682098
DOIs
StatePublished - Jul 12 2021
Event2021 IEEE International Symposium on Information Theory, ISIT 2021 - Virtual, Melbourne, Australia
Duration: Jul 12 2021Jul 20 2021

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2021-July
ISSN (Print)2157-8095

Conference

Conference2021 IEEE International Symposium on Information Theory, ISIT 2021
Country/TerritoryAustralia
CityVirtual, Melbourne
Period7/12/217/20/21

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Fingerprint

Dive into the research topics of 'Byzantine Resilient Distributed Clustering with Redundant Data Assignment'. Together they form a unique fingerprint.

Cite this