{"id":"https://openalex.org/W7160516101","doi":"https://doi.org/10.48550/arxiv.2605.04827","title":"Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity","display_name":"Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160516101","doi":"https://doi.org/10.48550/arxiv.2605.04827"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.04827","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134940881","display_name":"Junxiang Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Junxiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135590013","display_name":"Zhiqiang Kou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kou, Zhiqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135600719","display_name":"Hongwei Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Hongwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135630488","display_name":"Wenke Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Wenke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135621274","display_name":"Biao Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Biao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135565952","display_name":"Hanlin Gu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gu, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135598511","display_name":"Yuheng Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Yuheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128219769","display_name":"Di Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135628601","display_name":"Yang Liu","orcid":"https://orcid.org/0009-0008-5111-7248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135561099","display_name":"Xin Geng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Geng, Xin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.22100000083446503,"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":0.22100000083446503,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.22050000727176666,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.052299998700618744,"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/annotation","display_name":"Annotation","score":0.7088000178337097},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.578499972820282},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.557200014591217},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5508000254631042},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5166000127792358},{"id":"https://openalex.org/keywords/trustworthiness","display_name":"Trustworthiness","score":0.49239999055862427},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.3926999866962433},{"id":"https://openalex.org/keywords/isolation","display_name":"Isolation (microbiology)","score":0.3743000030517578}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8277999758720398},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.7088000178337097},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.578499972820282},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.557200014591217},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5508000254631042},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5166000127792358},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.42410001158714294},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4156999886035919},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4101000130176544},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3986000120639801},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.3926999866962433},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.3743000030517578},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.3723999857902527},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3668000102043152},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.35530000925064087},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.34630000591278076},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.3066999912261963},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2888999879360199},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25589999556541443},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.04827","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.04827","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Label":[0,41],"Distribution":[1,42],"Learning":[2,43],"(LDL)":[3],"models":[4],"supervision":[5,148],"as":[6],"an":[7],"instance-wise":[8],"probability":[9],"distribution,":[10],"enabling":[11],"fine-grained":[12],"learning":[13],"under":[14,146],"inherent":[15],"ambiguity,":[16],"but":[17],"its":[18],"success":[19],"relies":[20],"on":[21,161],"high-fidelity":[22],"label":[23],"distributions":[24],"that":[25,92,105,145],"are":[26],"costly":[27],"to":[28],"obtain":[29],"and":[30,60,100,131],"thus":[31],"often":[32],"noisy.":[33],"Motivated":[34],"by":[35,87,109],"privacy-sensitive":[36],"applications,":[37],"we":[38,71,122],"study":[39],"Federated":[40],"(Fed-LDL),":[44],"where":[45],"data":[46],"isolation":[47],"further":[48,139],"induces":[49],"heterogeneous":[50,147],"annotation":[51,135],"quality":[52,136],"across":[53],"clients,":[54],"making":[55],"local":[56],"updates":[57],"unevenly":[58],"reliable":[59,111],"breaking":[61],"sample-size-based":[62],"aggregation":[63,104],"(e.g.,":[64],"FedAvg).":[65],"To":[66,118],"address":[67],"this":[68],"trust":[69],"dilemma,":[70],"propose":[72],"FedQual,":[73],"a":[74,88,141],"quality-aware":[75],"Fed-LDL":[76,126],"framework":[77],"with":[78,133],"two":[79],"coupled":[80],"mechanisms:":[81],"(i)":[82],"quality-adaptive":[83],"client":[84,107],"training":[85],"guided":[86],"global":[89],"semantic":[90],"anchor":[91],"calibrates":[93],"low-quality":[94],"clients":[95],"while":[96],"preserving":[97],"high-quality":[98],"autonomy,":[99],"(ii)":[101],"reliability-aware":[102],"server":[103],"reweights":[106],"contributions":[108],"effective":[110],"information":[112],"rather":[113],"than":[114,155],"raw":[115],"sample":[116],"size.":[117],"enable":[119],"rigorous":[120],"evaluation,":[121],"construct":[123],"four":[124],"new":[125],"benchmarks":[127,164],"(FER-LDL,":[128],"FI-LDL,":[129],"PIPAL-LDL,":[130],"KADID-LDL)":[132],"controlled":[134],"disparity.":[137],"We":[138],"provide":[140],"theoretical":[142],"guarantee":[143],"showing":[144],"quality,":[149],"client-specific":[150],"calibration":[151],"is":[152],"strictly":[153],"better":[154],"any":[156],"uniform":[157],"calibration.":[158],"Extensive":[159],"experiments":[160],"the":[162,166],"proposed":[163],"demonstrate":[165],"effectiveness":[167],"of":[168],"FedQual.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-08T00:00:00"}
