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Massachusetts Institute of Technology

Wisdom of the machines : federated learning using OPAL

Abstract

dc:description.abstract

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.

Degree

thesis:*
Department dc:contributor.department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2018

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Alotaibi, Abdulrahman
Advisor dc:contributor.advisor
  • Alex 'Sandy' Pentland.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • 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.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/120686
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/120686

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Alotaibi, Abdulrahman. Wisdom of the machines : federated learning using OPAL. Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120686