{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:12:18Z","timestamp":1778080338272,"version":"3.51.4"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Key Research and Development Plan of China","award":["2020YFC2007104"],"award-info":[{"award-number":["2020YFC2007104"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61972383"],"award-info":[{"award-number":["61972383"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Science and Technology Service Network Initiative, Chinese Academy of Sciences","award":["KFJ-STS-QYZD-2021-11-001"],"award-info":[{"award-number":["KFJ-STS-QYZD-2021-11-001"]}]},{"DOI":"10.13039\/501100009592","name":"Beijing Municipal Science & Technology Commission","doi-asserted-by":"crossref","award":["Z211100002121171"],"award-info":[{"award-number":["Z211100002121171"]}],"id":[{"id":"10.13039\/501100009592","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jinan S&T Bureau","award":["2020GXRC030"],"award-info":[{"award-number":["2020GXRC030"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"<jats:p>\n            <jats:bold>Federated learning (FL)<\/jats:bold>\n            is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants\u2019 side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a\n            <jats:bold>Consensus-based Label Correction approach (CLC)<\/jats:bold>\n            in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/bixiao-zeng\/CLC.git\">https:\/\/github.com\/bixiao-zeng\/CLC.git<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3519311","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T14:20:27Z","timestamp":1647958827000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["CLC: A Consensus-based Label Correction Approach in Federated Learning"],"prefix":"10.1145","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1888-4209","authenticated-orcid":false,"given":"Bixiao","family":"Zeng","sequence":"first","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China and Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, China and Shandong Academy of Intelligent Computing Technology, Jinan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China and the Beijing Key Laboratory of Mobile Computing and Pervasive Device, China and Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanchao","family":"Yu","sequence":"additional","affiliation":[{"name":"Bureau of Frontier Sciences and Education, Chinese Academy of Sciences, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"International Conference on Learning Representations","author":"Acar Durmus Alp Emre","year":"2020","unstructured":"Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. 2020. 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