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    <journal-meta>
      <journal-id journal-id-type="nlm-ta">REA Press</journal-id>
      <journal-id journal-id-type="publisher-id">null</journal-id>
      <journal-title>REA Press</journal-title><issn pub-type="ppub">3042-0180</issn><issn pub-type="epub">3042-0180</issn><publisher>
      	<publisher-name>REA Press</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">https://doi.org/10.22105/scfa.v3i2.103</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group><subject>Federated pattern mining, Rare itemsets, Non-IID data, Differential privacy, Secure aggregation, Communication efficiency</subject></subj-group>
      </article-categories>
      <title-group>
        <article-title>Communication-Efficient Federated Rare-Pattern Mining under Non-IID Data and Privacy Constraints</article-title><subtitle>Communication-Efficient Federated Rare-Pattern Mining under Non-IID Data and Privacy Constraints</subtitle></title-group>
      <contrib-group><contrib contrib-type="author">
	<name name-style="western">
	<surname>Grover </surname>
		<given-names>Jyotsana </given-names>
	</name>
	<aff>Department of Computer Science and Information Systems, Birla Institute of Technology and Science, Pilani, India.</aff>
	</contrib><contrib contrib-type="author">
	<name name-style="western">
	<surname>Devi G T</surname>
		<given-names>Shakila </given-names>
	</name>
	<aff>Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, India.</aff>
	</contrib></contrib-group>		
      <pub-date pub-type="ppub">
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>09</day>
        <month>06</month>
        <year>2026</year>
      </pub-date>
      <volume>3</volume>
      <issue>2</issue>
      <permissions>
        <copyright-statement>© 2026 REA Press</copyright-statement>
        <copyright-year>2026</copyright-year>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/2.5/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p></license>
      </permissions>
      <related-article related-article-type="companion" vol="2" page="e235" id="RA1" ext-link-type="pmc">
			<article-title>Communication-Efficient Federated Rare-Pattern Mining under Non-IID Data and Privacy Constraints</article-title>
      </related-article>
	  <abstract abstract-type="toc">
		<p>
			Rare co-occurrence patterns can be difficult to discover across institutions whose transactions remain distributed. We present FedRPM, a bounded-candidate protocol combining noisy summaries, pooled support estimation, and a verification stage. The reported proof-of-concept campaign uses twenty eight-client federations of UCI Adult under two Dirichlet skew levels, ten seeds per level, six planted carrier-concentrated itemsets per run, and a centralized Apriori support-band reference. FedRPM achieves mean recall 0.9564 and precision 0.9999 for supports between 0.5 and 2.0% at 67.22 KB of serialized communication, compared with recall 0.9234, precision 0.9896, and 72.49 KB for naive broadcast (reported paired Wilcoxon W = 6, p = 2.67e-05). The full-lattice federated reference reaches recall 0.9926 at 82.59 KB, or 22.9% more communication than FedRPM. Reducing the nominal per-coordinate epsilon from 8 to 0.5 lowers recall to 0.9221. These epsilon settings describe count-noise scales, not a demonstrated total record-level privacy budget. Secure aggregation is simulated, transmitted-content bytes are an exposure surrogate rather than a privacy guarantee, and adaptive thresholds and the feasibility controller are untested. The results therefore characterize candidate coverage and communication in a small simulation; an audited transcript accountant and the experiment artifacts are required to establish end-to-end privacy and reproducibility.
		</p>
		</abstract>
    </article-meta>
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