{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/124159"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/124159","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient cross-silo federated learning using a computing power-aware scheduler","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","abstract_has_math":false,"creators":["Li, Zilinghan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Kindratenko, Volodymyr"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-05","date_published":"2024-05","updated_at":"2026-07-22T22:25:00Z","subjects":["Federated Learning","Device Heterogeneity","Cross-silo Federated Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Zilinghan Li"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/124159","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Kindratenko, Volodymyr"]},{"key":"dc:creator","label":"Author","values":["Li, Zilinghan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-05","2024-04-15"]},{"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","Device Heterogeneity","Cross-silo Federated 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 2024 Zilinghan Li"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/124159"]}]},{"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 2024-09-16 without embargo terms","The student, Zilinghan Li, accepted the attached license on 2024-04-12 at 14:46.","The student, Zilinghan Li, submitted this Thesis for approval on 2024-04-12 at 14:53.","This Thesis was approved for publication on 2024-04-15 at 10:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20271 on 2024-09-16 at 00:33:35","Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized machine learning models in domains such as healthcare, finance, and scientific projects lacking a centralized data repository, without transferring datasets and compromising the privacy of sensitive local data. Because of the disparity of computing resources among different federated learning clients (i.e., device heterogeneity), the efficiency of the prevalent synchronous federated learning algorithms is hindered by wasting time in waiting for the slow straggler clients. On the other hand, asynchronous federated learning algorithms face challenges with convergence rate and final model accuracy when dealing with non-identically and independently distributed (non-IID) heterogeneous datasets due to the negative impact of outdated local models and client drifting issues. To tackle these challenges in cross-silo federated learning with heterogeneous clients and data, we introduce FedCompass, an innovative semi-asynchronous federated learning algorithm featuring a server-side computing power-aware scheduler that adaptively assigns varying amounts of training tasks to different clients using the estimation of the computing power of individual clients. FedCompass ensures that multiple locally trained models are received for aggregation almost simultaneously, thereby reducing local model staleness and improving global model performance. Meanwhile, the overall training process remains asynchronous, eliminating delays caused by slow straggler clients. Through experiments with diverse non-IID federated datasets, we demonstrate that FedCompass can achieve faster convergence and higher accuracy compared to other asynchronous algorithms while maintaining superior efficiency over synchronous algorithms in scenarios involving heterogeneous clients. The code for FedCompass is open-sourced at https://github.com/APPFL/FedCompass."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Efficient cross-silo federated learning using a computing power-aware scheduler"]}]}],"canonical_facts":{"dc:contributor":["Kindratenko, Volodymyr"],"dc:creator":["Li, Zilinghan"],"dc:date":["2024-05","2024-04-15"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms","The student, Zilinghan Li, accepted the attached license on 2024-04-12 at 14:46.","The student, Zilinghan Li, submitted this Thesis for approval on 2024-04-12 at 14:53.","This Thesis was approved for publication on 2024-04-15 at 10:42.","DSpace SAF Submission Ingestion Package generated from Vireo submission #20271 on 2024-09-16 at 00:33:35","Cross-silo federated learning offers a promising solution to collaboratively train robust and generalized machine learning models in domains such as healthcare, finance, and scientific projects lacking a centralized data repository, without transferring datasets and compromising the privacy of sensitive local data. Because of the disparity of computing resources among different federated learning clients (i.e., device heterogeneity), the efficiency of the prevalent synchronous federated learning algorithms is hindered by wasting time in waiting for the slow straggler clients. On the other hand, asynchronous federated learning algorithms face challenges with convergence rate and final model accuracy when dealing with non-identically and independently distributed (non-IID) heterogeneous datasets due to the negative impact of outdated local models and client drifting issues. To tackle these challenges in cross-silo federated learning with heterogeneous clients and data, we introduce FedCompass, an innovative semi-asynchronous federated learning algorithm featuring a server-side computing power-aware scheduler that adaptively assigns varying amounts of training tasks to different clients using the estimation of the computing power of individual clients. FedCompass ensures that multiple locally trained models are received for aggregation almost simultaneously, thereby reducing local model staleness and improving global model performance. Meanwhile, the overall training process remains asynchronous, eliminating delays caused by slow straggler clients. Through experiments with diverse non-IID federated datasets, we demonstrate that FedCompass can achieve faster convergence and higher accuracy compared to other asynchronous algorithms while maintaining superior efficiency over synchronous algorithms in scenarios involving heterogeneous clients. The code for FedCompass is open-sourced at https://github.com/APPFL/FedCompass."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/124159"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Zilinghan Li"],"dc:subject":["Federated Learning","Device Heterogeneity","Cross-silo Federated Learning"],"dc:title":["Efficient cross-silo federated learning using a computing power-aware scheduler"],"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":"2026-07-22T22:25:00Z"}