{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(1)"],"submitter":["Zhang F"],"pubmed_abstract":["Federated learning (FL) is a promising approach for healthcare institutions to train high-quality medical models collaboratively while protecting sensitive data privacy. However, FL models encounter fairness issues at diverse levels, leading to performance disparities across different subpopulations. To address this, we propose Federated Learning with Unified Fairness Objective (FedUFO), a unified framework consolidating diverse fairness levels within FL. By leveraging distributionally robust optimization and a unified uncertainty set, it ensures consistent performance across all subpopulations and enhances the overall efficacy of FL in healthcare and other domains while maintaining accuracy levels comparable with those of existing methods. Our model was validated by applying it to four di"],"journal":["Patterns (New York, N.Y.)"],"pagination":["100907"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10801255"],"repository":["biostudies-literature"],"pubmed_title":["Unified fair federated learning for digital healthcare."],"pmcid":["PMC10801255"],"pubmed_authors":["Zhang F","Xiao J","Shuai Z","Wu F","Kuang K","Zhuang Y"],"additional_accession":[]},"is_claimable":false,"name":"Unified fair federated learning for digital healthcare.","description":"Federated learning (FL) is a promising approach for healthcare institutions to train high-quality medical models collaboratively while protecting sensitive data privacy. However, FL models encounter fairness issues at diverse levels, leading to performance disparities across different subpopulations. To address this, we propose Federated Learning with Unified Fairness Objective (FedUFO), a unified framework consolidating diverse fairness levels within FL. By leveraging distributionally robust optimization and a unified uncertainty set, it ensures consistent performance across all subpopulations and enhances the overall efficacy of FL in healthcare and other domains while maintaining accuracy levels comparable with those of existing methods. Our model was validated by applying it to four di","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jan","modification":"2025-04-04T21:16:14.136Z","creation":"2025-04-04T21:16:14.136Z"},"accession":"S-EPMC10801255","cross_references":{"pubmed":["38264718"],"doi":["10.1016/j.patter.2023.100907"]}}