{"id":"https://openalex.org/W4387969561","doi":"https://doi.org/10.1145/3581783.3611966","title":"FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data","display_name":"FedCD: A Classifier Debiased Federated Learning Framework for Non-IID Data","publication_year":2023,"publication_date":"2023-10-26","ids":{"openalex":"https://openalex.org/W4387969561","doi":"https://doi.org/10.1145/3581783.3611966"},"language":"en","primary_location":{"id":"doi:10.1145/3581783.3611966","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-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/A5101871806","display_name":"Yunfei Long","orcid":"https://orcid.org/0000-0003-1181-4387"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunfei Long","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1181-4387","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088046237","display_name":"Zhe Xue","orcid":"https://orcid.org/0000-0001-6123-0043"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Xue","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6123-0043","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007946897","display_name":"Lingyang Chu","orcid":"https://orcid.org/0000-0002-8937-1750"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Lingyang Chu","raw_affiliation_strings":["McMaster University, Hamilton, Canada"],"raw_orcid":"https://orcid.org/0000-0002-8937-1750","affiliations":[{"raw_affiliation_string":"McMaster University, Hamilton, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021175135","display_name":"Zhang Tian-long","orcid":"https://orcid.org/0009-0000-8258-6401"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianlong Zhang","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-8258-6401","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026309021","display_name":"Junjiang Wu","orcid":"https://orcid.org/0009-0006-0979-0506"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjiang Wu","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-0979-0506","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037336914","display_name":"Yu Zang","orcid":"https://orcid.org/0000-0001-6841-7388"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Zang","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-6841-7388","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100663187","display_name":"Junping Du","orcid":"https://orcid.org/0000-0001-8590-3767"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junping Du","raw_affiliation_strings":["Beijing University of Posts and Telecommunication, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8590-3767","affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunication, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.6106,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.91842867,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"8994","last_page":"9002"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994000196456909,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994000196456909,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9884999990463257,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9562000036239624,"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/classifier","display_name":"Classifier (UML)","score":0.8290152549743652},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8041229248046875},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7021494507789612},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6742249727249146},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4273979067802429},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3670874834060669},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3267439305782318}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.8290152549743652},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8041229248046875},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7021494507789612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6742249727249146},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4273979067802429},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3670874834060669},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3267439305782318}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3581783.3611966","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3581783.3611966","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1108969597","display_name":null,"funder_award_id":"62192784","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1520374204","display_name":null,"funder_award_id":"U22B2038","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G231118808","display_name":null,"funder_award_id":"62172056","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5534990446","display_name":null,"funder_award_id":"62272058","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320316083","display_name":"Tencent","ror":"https://ror.org/00hhjss72"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2744999500","https://openalex.org/W2993669487","https://openalex.org/W2995191368","https://openalex.org/W3027313259","https://openalex.org/W3035524453","https://openalex.org/W3038022836","https://openalex.org/W3092701607","https://openalex.org/W3108946571","https://openalex.org/W3112044954","https://openalex.org/W3127520698","https://openalex.org/W3166525903","https://openalex.org/W3167841610","https://openalex.org/W3173537849","https://openalex.org/W3176065502","https://openalex.org/W3182158470","https://openalex.org/W3187784948","https://openalex.org/W3204874618","https://openalex.org/W3213291156","https://openalex.org/W4225358669","https://openalex.org/W4225729075","https://openalex.org/W4226101686","https://openalex.org/W4226426325","https://openalex.org/W4283796083","https://openalex.org/W4294225672","https://openalex.org/W4312429505","https://openalex.org/W4312869277","https://openalex.org/W4379806213","https://openalex.org/W4385270425","https://openalex.org/W6600137863","https://openalex.org/W6759238902"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W4287823391","https://openalex.org/W3013363440","https://openalex.org/W4312762663","https://openalex.org/W4317941881","https://openalex.org/W3035927627","https://openalex.org/W3128909129","https://openalex.org/W4308527955","https://openalex.org/W2049864679"],"abstract_inverted_index":{"One":[0],"big":[1],"challenge":[2],"to":[3,20,67,106,142,147,164],"federated":[4,16,50,205],"learning":[5,17,51,65,206],"is":[6],"the":[7,38,77,86,90,99,108,116,125,128,132,144,157,166,177,184],"non-IID":[8,56],"data":[9],"distribution":[10,79,110],"caused":[11],"by":[12],"imbalanced":[13],"classes.":[14],"Existing":[15],"approaches":[18],"tend":[19],"bias":[21,126,182,201],"towards":[22],"classes":[23,170],"containing":[24],"a":[25,47,60,137],"larger":[26],"number":[27],"of":[28,93,119,127,131,168,179],"samples":[29],"during":[30],"local":[31,39,96,121,133,145],"updates,":[32],"which":[33,83],"causes":[34],"unwanted":[35],"drift":[36],"in":[37,89,171],"classifiers.":[40],"To":[41,123],"address":[42],"this":[43],"issue,":[44],"we":[45,102,135],"propose":[46],"classifier":[48,146,181,200],"debiased":[49],"framework":[52],"named":[53],"FedCD":[54],"for":[55,71],"data.":[57],"We":[58,153],"introduce":[59],"novel":[61],"hierarchical":[62],"prototype":[63],"contrastive":[64],"strategy":[66],"learn":[68,148],"fine-grained":[69,104],"prototypes":[70,75,105],"each":[72,81,112,120,172],"class.":[73],"The":[74],"characterize":[76],"sample":[78],"within":[80],"class,":[82],"helps":[84],"align":[85],"features":[87],"learned":[88],"representation":[91,100],"layer":[92,118,130],"every":[94],"client's":[95],"model.":[97,122],"At":[98],"layer,":[101],"use":[103],"rebalance":[107],"class":[109],"on":[111,161,183,190],"client":[113],"and":[114,202],"rectify":[115],"classification":[117,129,151],"alleviate":[124],"models,":[134],"incorporate":[136],"global":[138,150,185],"information":[139],"distillation":[140],"method":[141],"enable":[143],"decoupled":[149],"information.":[152],"also":[154],"adaptively":[155],"aggregate":[156],"class-level":[158],"classifiers":[159],"based":[160],"their":[162],"quality":[163],"reduce":[165],"impact":[167,178],"unreliable":[169],"aggregated":[173],"classifier.":[174,186],"This":[175],"mitigates":[176],"client-side":[180],"Comprehensive":[187],"experiments":[188],"conducted":[189],"various":[191],"datasets":[192],"show":[193],"that":[194],"our":[195],"method,":[196],"FedCD,":[197],"effectively":[198],"corrects":[199],"outperforms":[203],"state-of-the-art":[204],"methods.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}