{"id":"https://openalex.org/W4401596661","doi":"https://doi.org/10.1109/jiot.2024.3443995","title":"Incentivizing Efficient Label Denoising in Federated Learning","display_name":"Incentivizing Efficient Label Denoising in Federated Learning","publication_year":2024,"publication_date":"2024-08-14","ids":{"openalex":"https://openalex.org/W4401596661","doi":"https://doi.org/10.1109/jiot.2024.3443995"},"language":"en","primary_location":{"id":"doi:10.1109/jiot.2024.3443995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3443995","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"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/A5108577922","display_name":"Yizhou Yan","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yizhou Yan","raw_affiliation_strings":["Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0007-0437-3073","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xinyu Tang","orcid":"https://orcid.org/0009-0004-1721-3202"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyu Tang","raw_affiliation_strings":["Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0004-1721-3202","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101528010","display_name":"Chao Huang","orcid":"https://orcid.org/0000-0001-7309-2303"},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chao Huang","raw_affiliation_strings":["Department of Computer Science, The University of California at Davis, Davis, CA, USA"],"raw_orcid":"https://orcid.org/0000-0001-7309-2303","affiliations":[{"raw_affiliation_string":"Department of Computer Science, The University of California at Davis, Davis, CA, USA","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101643175","display_name":"Ming Tang","orcid":"https://orcid.org/0000-0003-4732-5155"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ming Tang","raw_affiliation_strings":["Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0003-4732-5155","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China","institution_ids":["https://openalex.org/I3045169105"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5108577922"],"corresponding_institution_ids":["https://openalex.org/I3045169105"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.10857964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"23","first_page":"38012","last_page":"38022"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12761","display_name":"Data Stream Mining Techniques","score":0.9628000259399414,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9628000259399414,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.9215999841690063,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T12535","display_name":"Machine Learning and Data Classification","score":0.901199996471405,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8290982246398926},{"id":"https://openalex.org/keywords/mnist-database","display_name":"MNIST database","score":0.6963965892791748},{"id":"https://openalex.org/keywords/incentive","display_name":"Incentive","score":0.6349083185195923},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5852891206741333},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.5637204051017761},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5096805095672607},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4867401123046875},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.46016523241996765},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4432287812232971},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.18740585446357727},{"id":"https://openalex.org/keywords/microeconomics","display_name":"Microeconomics","score":0.11939871311187744},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.08005544543266296}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8290982246398926},{"id":"https://openalex.org/C190502265","wikidata":"https://www.wikidata.org/wiki/Q17069496","display_name":"MNIST database","level":3,"score":0.6963965892791748},{"id":"https://openalex.org/C29122968","wikidata":"https://www.wikidata.org/wiki/Q1414816","display_name":"Incentive","level":2,"score":0.6349083185195923},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5852891206741333},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.5637204051017761},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5096805095672607},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4867401123046875},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.46016523241996765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4432287812232971},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.18740585446357727},{"id":"https://openalex.org/C175444787","wikidata":"https://www.wikidata.org/wiki/Q39072","display_name":"Microeconomics","level":1,"score":0.11939871311187744},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.08005544543266296},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jiot.2024.3443995","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jiot.2024.3443995","pdf_url":null,"source":{"id":"https://openalex.org/S2480266640","display_name":"IEEE Internet of Things Journal","issn_l":"2327-4662","issn":["2327-4662","2372-2541"],"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 Internet of Things Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2800729717","display_name":null,"funder_award_id":"62202214","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5706903555","display_name":null,"funder_award_id":"2023A1515012819","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4386603768","https://openalex.org/W2950475743","https://openalex.org/W2886711096","https://openalex.org/W2750384547","https://openalex.org/W4380078352","https://openalex.org/W3046591097","https://openalex.org/W4389249638","https://openalex.org/W3034789145","https://openalex.org/W4367628250","https://openalex.org/W4388819787"],"abstract_inverted_index":{"Federated":[0],"learning":[1,7],"(FL)":[2],"is":[3],"a":[4,14,88,108,129],"distributed":[5],"machine":[6],"scheme":[8],"that":[9,55,101,156,176,204,223],"enables":[10],"clients":[11,56,218],"to":[12,65,107,214,219,226],"train":[13],"shared":[15],"global":[16,36,110],"model":[17,75,82,111,210],"without":[18],"exchanging":[19],"local":[20],"data.":[21],"In":[22],"FL,":[23],"the":[24,32,35,52,74,83,95,102,114,121,135,141,147,182,185,189,195,209,227],"presence":[25],"of":[26,34,137,197],"label":[27,48,61,90,124],"noise":[28,149],"can":[29,159],"severely":[30],"reduce":[31],"accuracy":[33,112,211],"model.":[37],"Although":[38],"some":[39],"recent":[40],"works":[41],"have":[42],"focused":[43],"on":[44,73,171],"designing":[45],"algorithms":[46],"for":[47,139],"denoising,":[49],"they":[50],"ignored":[51],"important":[53],"issue":[54],"may":[57],"not":[58],"apply":[59],"costly":[60],"denoising":[62,91,125,144],"strategies":[63,222],"due":[64],"them":[66],"being":[67],"self-interested":[68],"and":[69,93,133,165,173,188,216],"having":[70],"heterogeneous":[71],"valuations":[72],"accuracy.":[76],"To":[77,119],"fill":[78],"this":[79],"gap,":[80],"we":[81,127],"clients\u2019":[84,96,122,142,178],"strategic":[85],"interactions":[86],"as":[87,177],"novel":[89],"game":[92],"determine":[94],"equilibrium":[97,103,186,221],"strategies.":[98],"We":[99,154,201],"prove":[100,155],"outcome":[104,187],"always":[105],"leads":[106],"lower":[109],"than":[113],"socially":[115,190,228],"optimal":[116,191,229],"solution":[117,192],"does.":[118],"motivate":[120],"efficient":[123],"behaviors,":[126,145],"propose":[128],"penalty-based":[130],"incentive":[131,199],"mechanism":[132,158,207],"design":[134],"degree":[136],"penalty":[138],"punishing":[140],"undesired":[143],"addressing":[146],"inaccurate":[148],"rate":[150],"detection":[151],"in":[152],"FL.":[153],"our":[157,205],"achieve":[160,220],"social":[161],"efficiency,":[162],"individual":[163],"rationality,":[164],"weak":[166],"budget":[167],"balance.":[168],"Numerical":[169],"experiments":[170],"MNIST":[172],"CIFAR-10":[174],"show":[175,203],"data":[179],"become":[180],"noisier,":[181],"gap":[183],"between":[184],"increases,":[193],"verifying":[194],"necessity":[196],"an":[198],"mechanism.":[200],"empirically":[202],"proposed":[206],"improves":[208],"by":[212],"up":[213],"4.4%":[215],"incentivizes":[217],"are":[224],"close":[225],"solution.":[230]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
