Conference paper Open Access

Online federated learning with imbalanced class distribution

Konstantinos Giorgas; Iraklis Varlamis


DataCite XML Export

<?xml version='1.0' encoding='utf-8'?>
<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd">
  <identifier identifierType="URL">https://zenodo.org/record/4728108</identifier>
  <creators>
    <creator>
      <creatorName>Konstantinos Giorgas</creatorName>
      <affiliation>Athens University of Economics and Business</affiliation>
    </creator>
    <creator>
      <creatorName>Iraklis Varlamis</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-0876-8167</nameIdentifier>
      <affiliation>Department of Informatics andTelematics, Harokopio University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Online federated learning with imbalanced class distribution</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <dates>
    <date dateType="Issued">2020-11-20</date>
  </dates>
  <resourceType resourceTypeGeneral="Text">Conference paper</resourceType>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4728108</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1145/3437120.3437282</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/teaching-h2020</relatedIdentifier>
  </relatedIdentifiers>
  <version>Authors' accepted manuscript</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&amp;nbsp; The federated learning paradigm can be a viable solution for handling huge datasets, and for taking advantage of powerful processing nodes on the edge. The process of online federated learning can be employed in order to maximise the potential of federated learning by re-training a shared model on the edge nodes and merging the updated models centrally. This approach allows edge nodes to exchange knowledge without exchanging their own training data, thus preserving their privacy. In this work, we examine the online federated learning approach in an extreme case of imbalanced class distribution between the central and the edge nodes. We examine the effects of different parameters of the online federated learning process and propose a technique that boosts the classification performance above that of the baseline centralised learning approach.&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/501100000780</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/871385/">871385</awardNumber>
      <awardTitle>A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence</awardTitle>
    </fundingReference>
  </fundingReferences>
</resource>
45
23
views
downloads
Views 45
Downloads 23
Data volume 24.3 MB
Unique views 38
Unique downloads 21

Share

Cite as