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<html xmlns="http://www.w3.org/1999/xhtml">
<title>1st Workshop on Federated Learning for Big Data</title>
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<h2>1st Workshop on Federated Learning for Big Data <br>in Conjunction with IEEE Big Data 2021 </h2>
<br><b>Submission Due</b>: Oct 1, 2021
<br><b>Notification Due</b>: Nov 1, 2021
<br><b>Camera Ready</b>: Nov 20, 2021
<br><b>Workshop Date</b>: Dec 15, 2021
<br><b>Venue</b>: Orlando, FL, USA
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<h3>Call for Papers</h3>
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<p>Machine learning models benefit from big, diverse training datasets. However, the sensitivity of the data and strict government regulations such as GDPR, CCPA, and HIPAA restrict organizations from sharing data. As such, entities with sensitive datasets can only develop locally optimal models. However, for robust, globally optimal models, that are highly generalizable, training needs to happen across organizational boundaries. Federated learning (FL) facilitates this by enabling the development of global models without sharing sensitive data.
<p>However, FL has challenges, such as privacy inferences, convergence, and bias concerns due to data heterogeneity amongst clients, security, cost, trust, regulation, etc. All these concerns have to be addressed to make FL practically scalable and useful.
<p>We are expecting broader, comprehensive, and greater application of these concepts and technologies and convergence towards holistic big data ecosystems. The goal of the workshop is to provide an open forum for industry experts, researchers, system-builders, and students interested in FL for Big Data.
<p><h3>Topics of interest include, but are not limited to, the following related to FL for Big Data </h3>
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<li>Incentive mechanisms and accountability including blockchain technologies</li>
<li>Adversarial attacks</li>
<li>Communication compression</li>
<li>Data heterogeneity</li>
<li>Fairness</li>
<li>Optimization advances</li>
<li>Partial participation</li>
<li>Open privacy challenges</li>
<li>Privacy-preserving FL approaches</li>
<li>Resource-efficiency</li>
<li>Systems and infrastructure</li>
<li>Theoretical contributions</li>
<li>Vertically partitioned datasets</li>
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<h3>Invited Talks</h3>
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<h3>Submission Instructions</h3>
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<p>Submissions are recommended (but not mandatory) to be no more than 6 pages long, excluding references, and follow <a href="" target="_blank">IEEE Big Data template</a>. Submissions are <b>single-blind</b>. An optional appendix of arbitrary length is allowed and should be put at the end of the paper (after references).
<p>Easychair submission link: <a href=" " target="_blank"></a>
<p>If you have any enquiries, please email us at: <a href="mailto:"></a>
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<h3>Organizing Committee</h3>
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<li>TBA</li>
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<h3>Program Committee</h3>
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<li>TBA</li>
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