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  <doi_batch_id>aspg-18-1622-1792025574</doi_batch_id>
  <timestamp>20261015005254</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
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 <body>
  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2022</year>
    </publication_date>
    <journal_volume>
     <volume>7</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Federated Knowledge Purification for Responsive Internet of Things</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Things Irina V.</given_name>
      <surname>Pustokhina</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Entrepreneurship and Logistics, Plekhanov Russian University of Economics, 117997, Moscow, Russia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Denis A.</given_name>
      <surname>Pustokhin</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Logistics, State University of Management, 109542, Moscow, Russia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>The Internet of Things (IoT) has become a ubiquitous technology that enables the collection and analysis of large amounts of data. However, the limited resources of IoT devices pose challenges to enabling responsive decision-making. Many communications are required for network training, yet network updates can be very big if they include many parameters. Participants and the IoT ecosystem both bear the brunt of federated learning's high Latency due to the magnitude of its communications infrastructure requirements. In this paper, we propose a Federated Knowledge Purification (FKP) approach based on dynamic reciprocal knowledge purification and adaptive gradient compression, two strategies that allow for low-latency communication without sacrificing effectiveness, which enables responsive IoT devices with limited resources. The FKP approach leverages a collaborative learning approach to enable IoT devices to learn from each other's experiences while preserving the privacy of their data. A smaller model is trained on the aggregated knowledge of a larger model trained on a centralized server, and this smaller model can be deployed on IoT devices to enable responsive decision-making with limited computational resources. Experimental results demonstrate the effectiveness of the proposed approach in improving the performance of IoT devices while maintaining the privacy of their data. The proposed approach also outperforms existing federated learning methods in terms of communication efficiency and convergence speed.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2022</year>
    </publication_date>
    <pages>
     <first_page>71</first_page>
     <last_page>80</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1622</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/JISIoT.070207</doi>
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       <resource mime_type="application/pdf">https://americaspg.com/storage/41678459927.pdf</resource>
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