{"id":"https://openalex.org/W4404629974","doi":"https://doi.org/10.1007/s40747-024-01636-4","title":"FL-Joint: joint aligning features and labels in federated learning for data heterogeneity","display_name":"FL-Joint: joint aligning features and labels in federated learning for data heterogeneity","publication_year":2024,"publication_date":"2024-11-23","ids":{"openalex":"https://openalex.org/W4404629974","doi":"https://doi.org/10.1007/s40747-024-01636-4"},"language":"en","primary_location":{"id":"doi:10.1007/s40747-024-01636-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-024-01636-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-024-01636-4.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex &amp; Intelligent Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s40747-024-01636-4.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100771953","display_name":"Wenxin Chen","orcid":"https://orcid.org/0000-0002-3641-9378"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenxin Chen","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, 410083, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, 410083, Hunan, China","institution_ids":["https://openalex.org/I139660479"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101961350","display_name":"Jinrui Zhang","orcid":"https://orcid.org/0000-0001-9035-3050"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jinrui Zhang","raw_affiliation_strings":["Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing, 100084, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100705648","display_name":"Deyu Zhang","orcid":"https://orcid.org/0000-0002-5676-1285"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deyu Zhang","raw_affiliation_strings":["School of Computer Science, Central South University, Changsha, 410083, Hunan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Central South University, Changsha, 410083, Hunan, China","institution_ids":["https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101961350"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":{"value":1320,"currency":"GBP","value_usd":1619},"apc_paid":{"value":1320,"currency":"GBP","value_usd":1619},"fwci":1.6724,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":{"value":0.87058846,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"11","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9980000257492065,"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":0.9980000257492065,"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":0.9438999891281128,"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":0.9239000082015991,"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":0.7436606287956238},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6268235445022583},{"id":"https://openalex.org/keywords/skew","display_name":"Skew","score":0.5530428290367126},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5457985401153564},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5324650406837463},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5222740173339844},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4694637358188629},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.46595579385757446},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4550503194332123},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.45150598883628845},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4286990761756897},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4219067394733429}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7436606287956238},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6268235445022583},{"id":"https://openalex.org/C43711488","wikidata":"https://www.wikidata.org/wiki/Q7534783","display_name":"Skew","level":2,"score":0.5530428290367126},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5457985401153564},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5324650406837463},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5222740173339844},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4694637358188629},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.46595579385757446},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4550503194332123},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.45150598883628845},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4286990761756897},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4219067394733429},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s40747-024-01636-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-024-01636-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-024-01636-4.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex &amp; Intelligent Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:c0a2d98072c74564b090f10c162dd700","is_oa":true,"landing_page_url":"https://doaj.org/article/c0a2d98072c74564b090f10c162dd700","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Complex & Intelligent Systems, Vol 11, Iss 1, Pp 1-14 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s40747-024-01636-4","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s40747-024-01636-4","pdf_url":"https://link.springer.com/content/pdf/10.1007/s40747-024-01636-4.pdf","source":{"id":"https://openalex.org/S3035462843","display_name":"Complex & Intelligent Systems","issn_l":"2198-6053","issn":["2198-6053","2199-4536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Complex &amp; Intelligent Systems","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4113732725","display_name":null,"funder_award_id":"2021JC0004","funder_id":"https://openalex.org/F4320330215","funder_display_name":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province"},{"id":"https://openalex.org/G5270258738","display_name":"Visual Problem-Solving in the Dolphin","funder_award_id":"6217243","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5507408182","display_name":null,"funder_award_id":"62172439","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7644884317","display_name":null,"funder_award_id":"2023CXQD061","funder_id":"https://openalex.org/F4320321514","funder_display_name":"Central South University"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321514","display_name":"Central South University","ror":"https://ror.org/00f1zfq44"},{"id":"https://openalex.org/F4320330215","display_name":"Natural Science Foundation for Distinguished Young Scholars of Hunan Province","ror":null},{"id":"https://openalex.org/F4320333688","display_name":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404629974.pdf","grobid_xml":"https://content.openalex.org/works/W4404629974.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1722318740","https://openalex.org/W2096943734","https://openalex.org/W2100659887","https://openalex.org/W2112796928","https://openalex.org/W2734358244","https://openalex.org/W2989289980","https://openalex.org/W3035667144","https://openalex.org/W3097060730","https://openalex.org/W3104055036","https://openalex.org/W3108566666","https://openalex.org/W3119381431","https://openalex.org/W3124383075","https://openalex.org/W3133814152","https://openalex.org/W3155912831","https://openalex.org/W3159080474","https://openalex.org/W3182158470","https://openalex.org/W3213772546","https://openalex.org/W4206320562","https://openalex.org/W4226461837","https://openalex.org/W4287332481","https://openalex.org/W4308350525","https://openalex.org/W4377240131","https://openalex.org/W4387105517","https://openalex.org/W4387717607","https://openalex.org/W6600103761"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2440023763","https://openalex.org/W2962474440","https://openalex.org/W2786094008","https://openalex.org/W3131501806","https://openalex.org/W2799683370","https://openalex.org/W2807745940"],"abstract_inverted_index":{"Federated":[0],"learning":[1,6,67],"is":[2],"a":[3,10,20,65,82,116],"distributed":[4],"machine":[5],"paradigm":[7],"that":[8,69,140],"trains":[9],"shared":[11,117],"model":[12],"using":[13,75,97],"data":[14,24,137],"from":[15,27],"various":[16],"clients,":[17],"it":[18],"faces":[19],"core":[21],"challenge":[22],"in":[23,50,56],"heterogeneity":[25],"arising":[26],"diverse":[28,133],"client":[29,110],"settings":[30,138],"and":[31,41,52,72,92,105,123,135,146],"environments.":[32],"Existing":[33],"methods":[34],"typically":[35],"focus":[36],"on":[37,102],"weight":[38],"divergence":[39],"mitigation":[40],"aggregation":[42],"strategy":[43],"enhancements,":[44],"they":[45],"overlook":[46],"the":[47,86,125],"mixed":[48],"skew":[49],"label":[51,71,91],"feature":[53,73,93,111,118],"distributions":[54,74,94,112],"prevalent":[55],"real-world":[57],"data.":[58],"To":[59],"address":[60],"this,":[61],"we":[62],"present":[63],"FL-Joint,":[64],"federated":[66],"framework":[68,80],"aligns":[70,90],"auxiliary":[76,98],"loss":[77,99],"functions.":[78],"This":[79,107],"involves":[81],"class-balanced":[83],"classifier":[84],"as":[85],"local":[87],"model.":[88],"It":[89],"locally":[95],"by":[96],"functions":[100],"based":[101],"class-conditional":[103],"information":[104],"pseudo-labels.":[106],"alignment":[108],"drives":[109],"to":[113,150],"converge":[114],"towards":[115],"space,":[119],"refining":[120],"decision":[121],"boundaries":[122],"boosting":[124],"global":[126],"model\u2019s":[127],"generalization":[128],"ability.":[129],"Extensive":[130],"experiments":[131],"across":[132],"datasets":[134],"heterogeneous":[136],"show":[139],"our":[141],"method":[142],"significantly":[143],"improves":[144],"accuracy":[145],"convergence":[147],"speed":[148],"compared":[149],"baseline":[151],"approaches.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}
