<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T15:45:23Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/120686" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/120686</identifier><datestamp>2021-07-05T14:03:20Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Alex 'Sandy' Pentland.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Alotaibi, Abdulrahman</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Program in Media Arts and Sciences (Massachusetts Institute of Technology)</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Program in Media Arts and Sciences (Massachusetts Institute of Technology)</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2019-03-01T19:58:13Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2019-03-01T19:58:13Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">http://hdl.handle.net/1721.1/120686</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1088561714</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 67-68).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Wisdom of the crowds (WOC) is an old concept that started by recording and aggregating people's estimations. It is one of the useful tools that exists today and allows many estimation applications to work correctly. Moreover, Open algorithms (OPAL) is a useful platform that enables institutions and individuals to share sensitive data, and increases the privacy of the data. In addition, federated learning is a new way to build and generate machine learning models by aggregating their hyperparameters. In this thesis, I show how to combine the three different concepts to build machine learning models on top of OPAL that utilize federated learning on a network. I then extend OPAL to support this new feature and demonstrate how to build a machine learning model using small independent models.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by Abdulrahman Alotaibi.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M.</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">68 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Program in Media Arts and Sciences ()</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Wisdom of the machines : federated learning using OPAL</dim:field>
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   	&lt;Title>Wisdom of the machines : federated learning using OPAL&lt;/Title>
   	&lt;Subtitle>Federated learning using OPAL&lt;/Subtitle>
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   	&lt;PublicationDate>2018&lt;/PublicationDate>
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        	&lt;DisplayName>Alotaibi, Abdulrahman&lt;/DisplayName>
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    &lt;Keyword>Program in Media Arts and Sciences ()&lt;/Keyword>
   	&lt;Abstract>Wisdom of the crowds (WOC) is an old concept that started by recording and aggregating people&amp;apos;s estimations. It is one of the useful tools that exists today and allows many estimation applications to work correctly. Moreover, Open algorithms (OPAL) is a useful platform that enables institutions and individuals to share sensitive data, and increases the privacy of the data. In addition, federated learning is a new way to build and generate machine learning models by aggregating their hyperparameters. In this thesis, I show how to combine the three different concepts to build machine learning models on top of OPAL that utilize federated learning on a network. I then extend OPAL to support this new feature and demonstrate how to build a machine learning model using small independent models.&lt;/Abstract>
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