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Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Federated learning (FL) is a promising technique for decentralized privacy-preserving Machine Learning (ML) with a diverse pool of participating devices with varying device capabilities. However, existing approaches to handle such heterogeneous environments do not consider \u201cfairness\u201d in model aggregation, resulting in significant performance variation among devices. Meanwhile, prior works on FL fairness remain hardware-oblivious and cannot be applied directly without severe performance penalties. To address this issue, we propose a novel hardware-sensitive FL method called\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathsf {FairHetero}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            that promotes fairness among heterogeneous federated clients. Our approach offers tunable fairness within a group of devices with the same ML architecture as well as across different groups with heterogeneous models. Our evaluation under\n            <jats:monospace>MNIST<\/jats:monospace>\n            ,\n            <jats:monospace>FEMNIST<\/jats:monospace>\n            ,\n            <jats:monospace>CIFAR10<\/jats:monospace>\n            , and\n            <jats:monospace>SHAKESPEARE<\/jats:monospace>\n            datasets demonstrates that\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathsf {FairHetero}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            can reduce variance among participating clients\u2019 test loss compared to the existing state-of-the-art techniques, resulting in increased overall performance.\n          <\/jats:p>","DOI":"10.1145\/3703627","type":"journal-article","created":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T10:04:12Z","timestamp":1731578652000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Hardware-Sensitive Fairness in Heterogeneous Federated Learning"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0930-3123","authenticated-orcid":false,"given":"Zahidur","family":"Talukder","sequence":"first","affiliation":[{"name":"The University of Texas at Arlington, Arlington, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6933-5135","authenticated-orcid":false,"given":"Bingqian","family":"Lu","sequence":"additional","affiliation":[{"name":"University of California Riverside, Riverside, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9003-4324","authenticated-orcid":false,"given":"Shaolei","family":"Ren","sequence":"additional","affiliation":[{"name":"University of California Riverside, Riverside, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5778-4366","authenticated-orcid":false,"given":"Mohammad Atiqul","family":"Islam","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, The University of Texas at Arlington, Arlington, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"29677","article-title":"Fedrolex: Model-heterogeneous federated learning with rolling sub-model extraction","volume":"35","author":"Alam Samiul","year":"2022","unstructured":"Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang. 2022. 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