{"id":"https://openalex.org/W4386322030","doi":"https://doi.org/10.1109/tnnls.2023.3306874","title":"Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise","display_name":"Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise","publication_year":2023,"publication_date":"2023-08-31","ids":{"openalex":"https://openalex.org/W4386322030","doi":"https://doi.org/10.1109/tnnls.2023.3306874","pmid":"https://pubmed.ncbi.nlm.nih.gov/37651486"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2023.3306874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3306874","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085331206","display_name":"Bixiao Zeng","orcid":"https://orcid.org/0000-0002-1888-4209"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bixiao Zeng","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1888-4209","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012133093","display_name":"Xiaodong Yang","orcid":"https://orcid.org/0000-0002-1842-5475"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Yang","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-1842-5475","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008558592","display_name":"Yiqiang Chen","orcid":"https://orcid.org/0000-0002-8407-0780"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiqiang Chen","raw_affiliation_strings":["Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-8407-0780","affiliations":[{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028988765","display_name":"Hanchao Yu","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanchao Yu","raw_affiliation_strings":["Bureau of Frontier Sciences and Education, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bureau of Frontier Sciences and Education, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043789501","display_name":"Chunyu Hu","orcid":"https://orcid.org/0000-0002-3238-9888"},"institutions":[{"id":"https://openalex.org/I152269853","display_name":"Qilu University of Technology","ror":"https://ror.org/04hyzq608","country_code":"CN","type":"education","lineage":["https://openalex.org/I152269853"]},{"id":"https://openalex.org/I4210142748","display_name":"Shandong Academy of Sciences","ror":"https://ror.org/04y8d6y55","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210142748"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunyu Hu","raw_affiliation_strings":["Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Qilu University of Technology (Shandong Academy of Sciences), Jinan, China","institution_ids":["https://openalex.org/I152269853","https://openalex.org/I4210142748"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101808278","display_name":"Yingwei Zhang","orcid":"https://orcid.org/0000-0002-6582-1745"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210090176","display_name":"Institute of Computing Technology","ror":"https://ror.org/0090r4d87","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210090176"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingwei Zhang","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China","Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-6582-1745","affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Computing Technology, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]},{"raw_affiliation_string":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210090176","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6526,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.75133874,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"35","issue":"12","first_page":"17620","last_page":"17634"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9866999983787537,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9229999780654907,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7883590459823608},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.7874487638473511},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6014950275421143},{"id":"https://openalex.org/keywords/gaussian-noise","display_name":"Gaussian noise","score":0.5075663328170776},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5041261911392212},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4991145133972168},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4886177182197571},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4873281419277191},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.422112375497818},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.391154944896698},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33816635608673096},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.12036633491516113}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883590459823608},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.7874487638473511},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6014950275421143},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.5075663328170776},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5041261911392212},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4991145133972168},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4886177182197571},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4873281419277191},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.422112375497818},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.391154944896698},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33816635608673096},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.12036633491516113},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tnnls.2023.3306874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2023.3306874","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Neural Networks and Learning Systems","raw_type":"journal-article"},{"id":"pmid:37651486","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/37651486","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on neural networks and learning systems","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6299999952316284,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G1636353083","display_name":null,"funder_award_id":"62202455","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1977690423","display_name":null,"funder_award_id":"Z211100002121171","funder_id":"https://openalex.org/F4320325902","funder_display_name":"Beijing Municipal Science and Technology Commission"},{"id":"https://openalex.org/G6956636849","display_name":null,"funder_award_id":"Z221100002722009","funder_id":"https://openalex.org/F4320325902","funder_display_name":"Beijing Municipal Science and Technology Commission"},{"id":"https://openalex.org/G8409499825","display_name":null,"funder_award_id":"202204910370","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"},{"id":"https://openalex.org/G8567002948","display_name":"\u9762\u5411\u5e15\u91d1\u68ee\u65e9\u671f\u9884\u8b66\u7684\u8054\u90a6\u8fc1\u79fb\u5b66\u4e60\u65b9\u6cd5\u7814\u7a76","funder_award_id":"61972383","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320325902","display_name":"Beijing Municipal Science and Technology Commission","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":56,"referenced_works":["https://openalex.org/W1921293667","https://openalex.org/W2048167005","https://openalex.org/W2073231946","https://openalex.org/W2108933033","https://openalex.org/W2167460663","https://openalex.org/W2577784528","https://openalex.org/W2592335154","https://openalex.org/W2598946096","https://openalex.org/W2765407302","https://openalex.org/W2889232360","https://openalex.org/W2901114541","https://openalex.org/W2964155802","https://openalex.org/W2964292098","https://openalex.org/W2970408908","https://openalex.org/W2972882814","https://openalex.org/W2981994674","https://openalex.org/W2985817549","https://openalex.org/W3012501605","https://openalex.org/W3019941393","https://openalex.org/W3021577186","https://openalex.org/W3034230713","https://openalex.org/W3042609801","https://openalex.org/W3118826713","https://openalex.org/W3155189475","https://openalex.org/W3159623990","https://openalex.org/W3165215947","https://openalex.org/W3182158470","https://openalex.org/W3196413773","https://openalex.org/W3208283650","https://openalex.org/W4220721049","https://openalex.org/W4223607725","https://openalex.org/W4255375128","https://openalex.org/W4300672471","https://openalex.org/W4310333713","https://openalex.org/W4312426709","https://openalex.org/W6639331287","https://openalex.org/W6677082149","https://openalex.org/W6728622933","https://openalex.org/W6728757088","https://openalex.org/W6738763127","https://openalex.org/W6740005241","https://openalex.org/W6746720608","https://openalex.org/W6751329665","https://openalex.org/W6751420435","https://openalex.org/W6751883947","https://openalex.org/W6752745768","https://openalex.org/W6752760542","https://openalex.org/W6759238902","https://openalex.org/W6762913911","https://openalex.org/W6771536673","https://openalex.org/W6784323503","https://openalex.org/W6786484107","https://openalex.org/W6786982071","https://openalex.org/W6787972765","https://openalex.org/W6797999826","https://openalex.org/W6802647179"],"related_works":["https://openalex.org/W2990323019","https://openalex.org/W2014494654","https://openalex.org/W3130349901","https://openalex.org/W1579833936","https://openalex.org/W2095350775","https://openalex.org/W1952261593","https://openalex.org/W1970319972","https://openalex.org/W1578916557","https://openalex.org/W2572355887","https://openalex.org/W2953254336"],"abstract_inverted_index":{"Federated":[0],"learning":[1,12],"(FL)":[2],"has":[3],"been":[4],"an":[5],"effective":[6],"way":[7],"to":[8,23,72,98,145,184,193,196],"train":[9],"a":[10,68,88,133],"machine":[11],"model":[13,99,120,189],"distributedly,":[14],"holding":[15],"local":[16,27,138],"data":[17],"without":[18],"exchanging":[19],"them.":[20],"However,":[21],"due":[22,195],"the":[24,74,80,124,127,147,163,173],"inaccessibility":[25],"of":[26,76,126,149],"data,":[28],"FL":[29,70,81,165,185],"with":[30],"label":[31,55,110],"noise":[32,45,56,77,101,111,199],"would":[33],"be":[34,58],"more":[35],"challenging.":[36],"Most":[37],"existing":[38],"methods":[39],"assume":[40],"only":[41],"open-set":[42],"or":[43,50],"closed-set":[44],"and":[46,78,115,139,170,172,178],"correspondingly":[47],"propose":[48,67],"filtering":[49],"correction":[51],"solutions,":[52],"ignoring":[53],"that":[54,91],"can":[57],"mixed":[59,82,109],"in":[60,95,112],"real-world":[61,174],"scenarios.":[62],"In":[63],"this":[64],"article,":[65],"we":[66],"novel":[69],"method":[71],"discriminate":[73],"type":[75],"make":[79],"noise-robust,":[83],"named":[84],"FedMIN.":[85],"FedMIN":[86,122,131,187],"employs":[87],"composite":[89],"framework":[90],"captures":[92],"local-global":[93],"differences":[94],"multiparticipant":[96],"distributions":[97],"generalized":[100],"patterns.":[102],"By":[103],"determining":[104],"adaptive":[105],"thresholds":[106],"for":[107],"identifying":[108],"each":[113],"client":[114],"assigning":[116],"appropriate":[117],"weights":[118],"during":[119],"aggregation,":[121],"enhances":[123],"performance":[125],"global":[128,140],"model.":[129],"Furthermore,":[130],"incorporates":[132],"loss":[134],"alignment":[135],"mechanism":[136],"using":[137],"Gaussian":[141],"mixture":[142],"models":[143],"(GMMs)":[144],"mitigate":[146],"risk":[148],"revealing":[150],"samplewise":[151],"loss.":[152],"Extensive":[153],"experiments":[154],"are":[155],"conducted":[156],"on":[157],"several":[158],"public":[159],"datasets,":[160],"which":[161],"include":[162],"simulated":[164],"testbeds,":[166],"i.e.,":[167,176],"CIFAR-10,":[168],"CIFAR-100,":[169],"SVHN,":[171],"ones,":[175],"Camelyon17":[177],"multiorgan":[179],"nuclei":[180],"challenge":[181],"(MoNuSAC).":[182],"Compared":[183],"benchmarks,":[186],"improves":[188],"accuracy":[190],"by":[191],"up":[192],"9.9%":[194],"its":[197],"superior":[198],"estimation":[200],"capabilities.":[201]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
