{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120432"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120432","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Federated domain adaptation for healthcare","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Jiang, Enyi"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Koyejo, Oluwasanmi"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Federated Learning","Domain Adaptation","Ml For Healthcare"],"languages":["en","eng"],"rights":["Copyright 2023 Enyi Jiang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120432","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Koyejo, Oluwasanmi"]},{"key":"dc:creator","label":"Author","values":["Jiang, Enyi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-02"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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","Domain Adaptation","Ml For Healthcare"]}]},{"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 2023 Enyi Jiang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120432"]}]},{"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 2025-05-01","The student, Enyi Jiang, accepted the attached license on 2023-04-27 at 14:51.","The student, Enyi Jiang, submitted this Thesis for approval on 2023-04-27 at 14:56.","This Thesis was approved for publication on 2023-05-02 at 14:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19217 on 2023-09-01 at 17:15:14","Federated domain adaptation (FDA) describes the setting where a set of source clients seek to optimize the performance of a target client. To be effective, FDA must address some of the distributional challenges of Federated learning (FL). For instance, FL systems exhibit distribution shifts across clients. Further, labeled data are not always available among the clients. To this end, we propose and compare novel approaches for FDA, combining the few labeled target samples with the source data when auxiliary labels are available to the clients. The in-distribution auxiliary information is included during local training to boost out-of-domain accuracy. Also, during fine-tuning, we devise a simple yet efficient gradient projection method (FedGP) to detect the valuable components from each source client model by comparing them with the target direction. The extensive experiments on healthcare datasets show that our proposed framework outperforms the state-of-the-art unsupervised FDA methods with limited additional time and space complexity. Additionally, we find that common techniques such as FedAvg and fine-tuning fail with a large domain shift. To better investigate the effectiveness of FedGP under various extents of domain shift, we perform extensive semi-synthetic and real-world experiments on general-purposed datasets compared with several baselines. Our results indicate a bias-variance trade-off between source and target domains when combining source and target gradients. FedGP maintains a better trade-off between source gradients' bias (the domain shift between source and target domains) and the target gradient's variance from limited labeled data. Our experiments illustrate the effectiveness of the proposed method in practice."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Federated domain adaptation for healthcare"]}]}],"canonical_facts":{"dc:contributor":["Koyejo, Oluwasanmi"],"dc:creator":["Jiang, Enyi"],"dc:date":["2023-05","2023-05-02"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-05-01","The student, Enyi Jiang, accepted the attached license on 2023-04-27 at 14:51.","The student, Enyi Jiang, submitted this Thesis for approval on 2023-04-27 at 14:56.","This Thesis was approved for publication on 2023-05-02 at 14:32.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19217 on 2023-09-01 at 17:15:14","Federated domain adaptation (FDA) describes the setting where a set of source clients seek to optimize the performance of a target client. To be effective, FDA must address some of the distributional challenges of Federated learning (FL). For instance, FL systems exhibit distribution shifts across clients. Further, labeled data are not always available among the clients. To this end, we propose and compare novel approaches for FDA, combining the few labeled target samples with the source data when auxiliary labels are available to the clients. The in-distribution auxiliary information is included during local training to boost out-of-domain accuracy. Also, during fine-tuning, we devise a simple yet efficient gradient projection method (FedGP) to detect the valuable components from each source client model by comparing them with the target direction. The extensive experiments on healthcare datasets show that our proposed framework outperforms the state-of-the-art unsupervised FDA methods with limited additional time and space complexity. Additionally, we find that common techniques such as FedAvg and fine-tuning fail with a large domain shift. To better investigate the effectiveness of FedGP under various extents of domain shift, we perform extensive semi-synthetic and real-world experiments on general-purposed datasets compared with several baselines. Our results indicate a bias-variance trade-off between source and target domains when combining source and target gradients. FedGP maintains a better trade-off between source gradients' bias (the domain shift between source and target domains) and the target gradient's variance from limited labeled data. Our experiments illustrate the effectiveness of the proposed method in practice."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120432"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Enyi Jiang"],"dc:subject":["Federated Learning","Domain Adaptation","Ml For Healthcare"],"dc:title":["Federated domain adaptation for healthcare"],"dc:type":["text"],"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":"2026-07-22T22:24:57Z"}