{"id":"https://openalex.org/W4408354184","doi":"https://doi.org/10.1109/icassp49660.2025.10889227","title":"FedSe: Group-Based Sequential Training Strategies for Mitigating Label Skew in Federated Learning","display_name":"FedSe: Group-Based Sequential Training Strategies for Mitigating Label Skew in Federated Learning","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354184","doi":"https://doi.org/10.1109/icassp49660.2025.10889227"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5103014423","display_name":"Kun Qiao","orcid":"https://orcid.org/0000-0003-2490-260X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210106108","display_name":"Xinjiang Technical Institute of Physics & Chemistry","ror":"https://ror.org/00x44h034","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210106108"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ketu Qiao","raw_affiliation_strings":["Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China","institution_ids":["https://openalex.org/I4210106108","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115695469","display_name":"Yi Wang","orcid":"https://orcid.org/0000-0001-9081-120X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210106108","display_name":"Xinjiang Technical Institute of Physics & Chemistry","ror":"https://ror.org/00x44h034","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210106108"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Wang","raw_affiliation_strings":["Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China","institution_ids":["https://openalex.org/I4210106108","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013751828","display_name":"Baoquan Wang","orcid":"https://orcid.org/0000-0001-6475-8513"},"institutions":[{"id":"https://openalex.org/I4210106108","display_name":"Xinjiang Technical Institute of Physics & Chemistry","ror":"https://ror.org/00x44h034","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210106108"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoquan Wang","raw_affiliation_strings":["Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China","institution_ids":["https://openalex.org/I4210106108","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101464308","display_name":"Zhengdong Luo","orcid":"https://orcid.org/0009-0005-8745-8528"},"institutions":[{"id":"https://openalex.org/I4210106108","display_name":"Xinjiang Technical Institute of Physics & Chemistry","ror":"https://ror.org/00x44h034","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210106108"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhengdong Luo","raw_affiliation_strings":["Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China","institution_ids":["https://openalex.org/I4210106108","https://openalex.org/I19820366"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5005362150","display_name":"Xi Zhou","orcid":"https://orcid.org/0000-0002-6736-7160"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210106108","display_name":"Xinjiang Technical Institute of Physics & Chemistry","ror":"https://ror.org/00x44h034","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210106108"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xi Zhou","raw_affiliation_strings":["Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Xinjiang Technical Institute of Physics & Chemistry,Urumqi,China","institution_ids":["https://openalex.org/I4210106108","https://openalex.org/I19820366"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103014423"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210106108"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06222276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14484","display_name":"Technology and Data Analysis","score":0.871399998664856,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T14484","display_name":"Technology and Data Analysis","score":0.871399998664856,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8025000095367432,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.7174999713897705,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.871691107749939},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7940618395805359},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.6954018473625183},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.4879435896873474},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4317699670791626},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36288851499557495},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.08971536159515381}],"concepts":[{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.871691107749939},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7940618395805359},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.6954018473625183},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.4879435896873474},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4317699670791626},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36288851499557495},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.08971536159515381},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889227","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889227","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W114517082","https://openalex.org/W2109426455","https://openalex.org/W2194775991","https://openalex.org/W2530417694","https://openalex.org/W2903890850","https://openalex.org/W2936503027","https://openalex.org/W2995022099","https://openalex.org/W2997721570","https://openalex.org/W3021654819","https://openalex.org/W3033511014","https://openalex.org/W3168213397","https://openalex.org/W3182158470","https://openalex.org/W3193066552","https://openalex.org/W3194243671","https://openalex.org/W3209696639","https://openalex.org/W4224227775","https://openalex.org/W4287332481","https://openalex.org/W4312950667","https://openalex.org/W4393159708","https://openalex.org/W6637373629","https://openalex.org/W6703116779","https://openalex.org/W6728757088","https://openalex.org/W6743688258","https://openalex.org/W6752029299","https://openalex.org/W6755988804","https://openalex.org/W6759226220","https://openalex.org/W6759238902","https://openalex.org/W6765541894","https://openalex.org/W6767676916","https://openalex.org/W6772307254","https://openalex.org/W6773817997","https://openalex.org/W6776730200","https://openalex.org/W6779676466","https://openalex.org/W6780224944","https://openalex.org/W6784106160","https://openalex.org/W6784239669","https://openalex.org/W6787972765","https://openalex.org/W6790034021","https://openalex.org/W6796484261","https://openalex.org/W6839362776","https://openalex.org/W6843195772"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W3046775127","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474"],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"emerged":[4],"as":[5],"a":[6,51,98],"promising":[7],"approach":[8],"for":[9],"distributed":[10],"machine":[11],"learning,":[12],"enabling":[13],"clients":[14,82],"to":[15,28,50,92],"collaboratively":[16],"train":[17],"models":[18,47,106,126],"without":[19],"sharing":[20],"their":[21],"data.":[22],"However,":[23],"existing":[24],"FL":[25],"methods":[26],"continue":[27],"face":[29],"challenges":[30],"when":[31],"dealing":[32],"with":[33,135],"non-IID":[34],"data,":[35],"particularly":[36],"under":[37],"conditions":[38],"of":[39,57,89,101,141],"extreme":[40],"label":[41,136],"skew.":[42],"This":[43],"divergence":[44],"among":[45],"client":[46],"can":[48],"lead":[49],"significant":[52],"degradation":[53],"in":[54,116],"the":[55,58,68,87,125,139],"accuracy":[56],"global":[59],"model.":[60],"To":[61],"address":[62],"this":[63],"critical":[64],"issue,":[65],"we":[66],"introduce":[67],"FedSe":[69],"approach,":[70],"which":[71],"incorporates":[72],"two":[73],"novel":[74],"components:":[75],"homogeneous":[76],"grouping":[77],"and":[78,143],"sequential":[79],"training.":[80],"First,":[81],"are":[83,107],"grouped":[84],"based":[85],"on":[86,132],"distribution":[88],"data":[90],"labels":[91],"ensure":[93],"that":[94],"each":[95],"group":[96],"contains":[97],"balanced":[99],"representation":[100],"all":[102],"labels.":[103],"Second,":[104],"task":[105],"trained":[108],"sequentially":[109],"within":[110],"these":[111],"groups":[112],"while":[113],"training":[114],"occurs":[115],"parallel":[117],"across":[118],"groups.":[119],"The":[120],"final":[121],"step":[122],"involves":[123],"aggregating":[124],"through":[127],"averaging.":[128],"Extensive":[129],"experimental":[130],"results":[131,145],"several":[133],"datasets":[134],"skew":[137],"demonstrate":[138],"effectiveness":[140],"FedSe,":[142],"its":[144],"surpass":[146],"current":[147],"state-of-the-art":[148],"methods.":[149]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}