{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/115643"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/115643","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Secure and scalable robust federated learning","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","abstract_has_math":false,"creators":["Liu, Andrew"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Khurana, Dakshita"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05","date_published":"2022-05","updated_at":"2026-07-22T22:24:54Z","subjects":["federated learning","secure multi-party computation","privacy preserving machine learning"],"languages":["en","eng"],"rights":["Copyright 2022 Andrew Liu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/115643","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Khurana, Dakshita"]},{"key":"dc:creator","label":"Author","values":["Liu, Andrew"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-05","2022-04-26"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"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","secure multi-party computation","privacy preserving machine learning"]}]},{"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 Andrew Liu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/115643"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Andrew Liu, accepted the attached license on 2022-04-20 at 13:15.","The student, Andrew Liu, submitted this Thesis for approval on 2022-04-20 at 13:22.","This Thesis was approved for publication on 2022-04-26 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17847 on 2022-11-14 at 00:24:50","With the rise of cloud computing in recent years, an increasing amount of data storage and computation are being offloaded from individual users’ computers to those of large corporations. These corporations benefit from merging their clients’ data, e.g. hospitals sharing client chest x-ray images to better detect and treat COVID-19. However, these institutions are restricted from sharing their client data due to both ethical and legal reasons. Federated learning is a technique that attempts to solve this problem by training a model across multiple edge devices while data stays on-device. This thesis focuses on the specific case of robust federated learning, where the central server coordinating the parties computes a robust aggregate of client updates to redistribute back as a global model. Cryptographic protocols such as secure multi-party computation and differential privacy are often added on top of federated learning to formally prove security. This work focuses on developing an efficient and private version of federated learning, when coordinate-wise median is used as a robust aggregator. A semi-honest protocol is developed for both median computation and approximate median computation. The convergence and robustness of this protocols is evaluated empirically."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Secure and scalable robust federated learning"]}]}],"canonical_facts":{"dc:contributor":["Khurana, Dakshita"],"dc:creator":["Liu, Andrew"],"dc:date":["2022-05","2022-04-26"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01","The student, Andrew Liu, accepted the attached license on 2022-04-20 at 13:15.","The student, Andrew Liu, submitted this Thesis for approval on 2022-04-20 at 13:22.","This Thesis was approved for publication on 2022-04-26 at 14:54.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17847 on 2022-11-14 at 00:24:50","With the rise of cloud computing in recent years, an increasing amount of data storage and computation are being offloaded from individual users’ computers to those of large corporations. These corporations benefit from merging their clients’ data, e.g. hospitals sharing client chest x-ray images to better detect and treat COVID-19. However, these institutions are restricted from sharing their client data due to both ethical and legal reasons. Federated learning is a technique that attempts to solve this problem by training a model across multiple edge devices while data stays on-device. This thesis focuses on the specific case of robust federated learning, where the central server coordinating the parties computes a robust aggregate of client updates to redistribute back as a global model. Cryptographic protocols such as secure multi-party computation and differential privacy are often added on top of federated learning to formally prove security. This work focuses on developing an efficient and private version of federated learning, when coordinate-wise median is used as a robust aggregator. A semi-honest protocol is developed for both median computation and approximate median computation. The convergence and robustness of this protocols is evaluated empirically."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/115643"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Andrew Liu"],"dc:subject":["federated learning","secure multi-party computation","privacy preserving machine learning"],"dc:title":["Secure and scalable robust federated learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:54Z"}