Asynchronous Distributed Edge-Variant Graph Filters

Mario Coutiño , Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
6 Downloads (Pure)


As the size of the sensor network grows, synchronization starts to become the main bottleneck for distributed computing. As a result, efforts in several areas have been focused on the convergence analysis of asynchronous computational methods. In this work, we aim to cross-pollinate distributed graph filters with results in parallel computing to provide guarantees for asynchronous graph filtering. To alleviate the possible reduction of convergence speed due to asynchronous updates, we also show how a slight modification to the graph filter recursion, through operator splitting, can be performed to obtain faster convergence. Finally, through numerical experiments the performance of the discussed methods is illustrated.

Original languageEnglish
Title of host publication2019 IEEE Data Science Workshop (DSW)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
Number of pages5
ISBN (Electronic)978-1-7281-0708-0
ISBN (Print) 978-1-7281-0709-7
Publication statusPublished - 2019
Event2019 IEEE Data Science Workshop, DSW 2019 - Minneapolis, United States
Duration: 2 Jun 20195 Jun 2019


Conference2019 IEEE Data Science Workshop, DSW 2019
CountryUnited States

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • asynchronous filtering
  • distributed signal processing
  • edge-variant graph filters
  • graph filters
  • graph signal processing


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