{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117764"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117764","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Flexible and lightweight toolbox for federated learning on edge devices","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Biskup, Dean"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Smaragdis, Paris"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Federated Learning","Machine Learning","Speech Enhancement"],"languages":["en","eng"],"rights":["Copyright 2022 Dean Biskup"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117764","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Smaragdis, Paris"]},{"key":"dc:creator","label":"Author","values":["Biskup, Dean"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-17"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Federated Learning","Machine Learning","Speech Enhancement"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Dean Biskup"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117764"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Dean Biskup, accepted the attached license on 2022-11-17 at 02:30.","The student, Dean Biskup, submitted this Thesis for approval on 2022-11-17 at 02:40.","This Thesis was approved for publication on 2022-11-17 at 16:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18593 on 2023-04-12 at 07:28:20","As edge devices with data collection capabilities, such as cell phones, home assistants, or autonomous vehicles become more ubiquitous, there has been a rapid increase in the amount of data collected. While this data is valuable for machine learning applications, there is an increasing demand for data privacy and effective local data processing to lower network bandwidth requirements. Federated Learning has emerged as a central paradigm of machine learning to tackle these issues, allowing for collaborative learning between many edge devices without requiring the sharing of sensitive data. To facilitate research into federated learning on real-world devices, this thesis work introduces the Federated Learning on Edge Systems (FLoES) software library, a flexible and lightweight federated learning toolbox that is targeted to run on single-board computers. The source code is available at: https://www.github.com/dbisk/floes."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Flexible and lightweight toolbox for federated learning on edge devices"]}]}],"canonical_facts":{"dc:contributor":["Smaragdis, Paris"],"dc:creator":["Biskup, Dean"],"dc:date":["2022-12","2022-11-17"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Dean Biskup, accepted the attached license on 2022-11-17 at 02:30.","The student, Dean Biskup, submitted this Thesis for approval on 2022-11-17 at 02:40.","This Thesis was approved for publication on 2022-11-17 at 16:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18593 on 2023-04-12 at 07:28:20","As edge devices with data collection capabilities, such as cell phones, home assistants, or autonomous vehicles become more ubiquitous, there has been a rapid increase in the amount of data collected. While this data is valuable for machine learning applications, there is an increasing demand for data privacy and effective local data processing to lower network bandwidth requirements. Federated Learning has emerged as a central paradigm of machine learning to tackle these issues, allowing for collaborative learning between many edge devices without requiring the sharing of sensitive data. To facilitate research into federated learning on real-world devices, this thesis work introduces the Federated Learning on Edge Systems (FLoES) software library, a flexible and lightweight federated learning toolbox that is targeted to run on single-board computers. The source code is available at: https://www.github.com/dbisk/floes."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117764"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Dean Biskup"],"dc:subject":["Federated Learning","Machine Learning","Speech Enhancement"],"dc:title":["Flexible and lightweight toolbox for federated learning on edge devices"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}