{"id":"https://openalex.org/W4414348584","doi":"https://doi.org/10.1109/tpami.2025.3612302","title":"Sample-Level Prototypical Federated Learning","display_name":"Sample-Level Prototypical Federated Learning","publication_year":2025,"publication_date":"2025-09-19","ids":{"openalex":"https://openalex.org/W4414348584","doi":"https://doi.org/10.1109/tpami.2025.3612302","pmid":"https://pubmed.ncbi.nlm.nih.gov/40971273"},"language":"en","primary_location":{"id":"doi:10.1109/tpami.2025.3612302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3612302","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5113950414","display_name":"Chuizheng Meng","orcid":null},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuizheng Meng","raw_affiliation_strings":["Department of Computer Science, University of Southern California, Los Angeles, CA, USA","Department of Computer Science, University of Southern California, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0002-5020-7697","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Department of Computer Science, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jianke Yang","orcid":"https://orcid.org/0000-0001-7518-6848"},"institutions":[{"id":"https://openalex.org/I36258959","display_name":"University of California San Diego","ror":"https://ror.org/0168r3w48","country_code":"US","type":"education","lineage":["https://openalex.org/I36258959"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianke Yang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA","Department of Computer Science and Engineering, University of California San Diego, La Jolla, USA"],"raw_orcid":"https://orcid.org/0000-0001-7518-6848","affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California San Diego, San Diego, CA, USA","institution_ids":["https://openalex.org/I36258959"]},{"raw_affiliation_string":"Department of Computer Science and Engineering, University of California San Diego, La Jolla, USA","institution_ids":["https://openalex.org/I36258959"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100619561","display_name":"Hao Niu","orcid":"https://orcid.org/0000-0002-5623-9470"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hao Niu","raw_affiliation_strings":["KDDI Research, Inc., Fujimino, Japan"],"raw_orcid":"https://orcid.org/0000-0002-5623-9470","affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015450941","display_name":"Guillaume Habault","orcid":"https://orcid.org/0000-0002-3364-5863"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Guillaume Habault","raw_affiliation_strings":["KDDI Research, Inc., Fujimino, Japan"],"raw_orcid":"https://orcid.org/0000-0002-3364-5863","affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014746910","display_name":"Roberto Legaspi","orcid":"https://orcid.org/0000-0001-8909-635X"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Roberto Legaspi","raw_affiliation_strings":["KDDI Research, Inc., Fujimino, Japan"],"raw_orcid":"https://orcid.org/0000-0001-8909-635X","affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062786880","display_name":"Shinya Wada","orcid":"https://orcid.org/0000-0001-6009-6655"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shinya Wada","raw_affiliation_strings":["KDDI Research, Inc., Fujimino, Japan"],"raw_orcid":"https://orcid.org/0000-0001-6009-6655","affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033829205","display_name":"Chihiro Ono","orcid":"https://orcid.org/0000-0002-6410-1359"},"institutions":[{"id":"https://openalex.org/I4210164495","display_name":"KDDI Research (Japan)","ror":"https://ror.org/05qsqt662","country_code":"JP","type":"company","lineage":["https://openalex.org/I4210164495"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Chihiro Ono","raw_affiliation_strings":["KDDI Research, Inc., Fujimino, Japan"],"raw_orcid":"https://orcid.org/0000-0002-6410-1359","affiliations":[{"raw_affiliation_string":"KDDI Research, Inc., Fujimino, Japan","institution_ids":["https://openalex.org/I4210164495"]}]},{"author_position":"last","author":{"id":null,"display_name":"Yan Liu","orcid":"https://orcid.org/0000-0002-7055-9518"},"institutions":[{"id":"https://openalex.org/I1174212","display_name":"University of Southern California","ror":"https://ror.org/03taz7m60","country_code":"US","type":"education","lineage":["https://openalex.org/I1174212"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yan Liu","raw_affiliation_strings":["Department of Computer Science, University of Southern California, Los Angeles, CA, USA","Department of Computer Science, University of Southern California, Los Angeles, USA"],"raw_orcid":"https://orcid.org/0000-0002-7055-9518","affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Southern California, Los Angeles, CA, USA","institution_ids":["https://openalex.org/I1174212"]},{"raw_affiliation_string":"Department of Computer Science, University of Southern California, Los Angeles, USA","institution_ids":["https://openalex.org/I1174212"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":8,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11813782,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"48","issue":"2","first_page":"1133","last_page":"1144"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9987999796867371,"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":0.9987999796867371,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.930899977684021,"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/federated-learning","display_name":"Federated learning","score":0.7181000113487244},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.5849999785423279},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5673999786376953},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.5238999724388123},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5060999989509583},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4945000112056732},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.43970000743865967},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.41260001063346863}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8564000129699707},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7181000113487244},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5673999786376953},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5060999989509583},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47380000352859497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45660001039505005},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.43970000743865967},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.41260001063346863},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.39419999718666077},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.3481000065803528},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.3294000029563904},{"id":"https://openalex.org/C142039133","wikidata":"https://www.wikidata.org/wiki/Q3620943","display_name":"Personalized learning","level":5,"score":0.3237999975681305},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3163999915122986},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.30640000104904175},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C100463513","wikidata":"https://www.wikidata.org/wiki/Q5227322","display_name":"Data model (GIS)","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25760000944137573}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tpami.2025.3612302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpami.2025.3612302","pdf_url":null,"source":{"id":"https://openalex.org/S199944782","display_name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","issn_l":"0162-8828","issn":["0162-8828","1939-3539","2160-9292"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320439","host_organization_name":"IEEE Computer Society","host_organization_lineage":["https://openalex.org/P4310320439","https://openalex.org/P4310319808"],"host_organization_lineage_names":["IEEE Computer Society","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 Transactions on Pattern Analysis and Machine Intelligence","raw_type":"journal-article"},{"id":"pmid:40971273","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/40971273","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE transactions on pattern analysis and machine intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2883725317","https://openalex.org/W2891400669","https://openalex.org/W2911752602","https://openalex.org/W2970606380","https://openalex.org/W2989289980","https://openalex.org/W2995022099","https://openalex.org/W3011304563","https://openalex.org/W3016632787","https://openalex.org/W3018102029","https://openalex.org/W3035524453","https://openalex.org/W3080670519","https://openalex.org/W3080934299","https://openalex.org/W3081178085","https://openalex.org/W3091635927","https://openalex.org/W4200631596","https://openalex.org/W4382239439"],"related_works":[],"abstract_inverted_index":{"With":[0],"the":[1,48,54,80,106,124,129,159,166],"increasing":[2],"concerns":[3],"about":[4],"privacy":[5],"and":[6,47,119,169,229,236],"data":[7,26,34,36,73,92,117,131,139,177,197],"regulations,":[8],"federated":[9],"learning":[10,21,164,181],"(FL)":[11],"has":[12,51],"been":[13,53],"emerging":[14],"as":[15],"a":[16,31,171,218],"solution":[17],"to":[18,104,157],"train":[19],"machine":[20],"models":[22,122,212,224],"collaboratively":[23],"with":[24,95,209,217],"non-exchangeable":[25],"from":[27,74,93,232],"multiple":[28,75],"clients.":[29],"As":[30],"result":[32],"of":[33,180,188],"locality,":[35],"is":[37,65],"usually":[38],"not":[39],"identically":[40],"or":[41,136,221],"independently":[42],"(non-IID)":[43],"distributed":[44],"across":[45,109],"clients,":[46],"non-IID":[49,81,107],"property":[50],"long":[52],"key":[55],"challenge":[56,108],"in":[57,60,85,146],"FL.":[58],"Furthermore,":[59],"real-world":[61,147,227],"cross-silo":[62],"scenarios,":[63],"it":[64,192],"ubiquitous":[66],"that":[67,205],"clients":[68,110],"are":[69,143],"organizations":[70],"owning":[71],"private":[72],"domains":[76],"internally,":[77],"which":[78,142],"exacerbates":[79],"issue.":[82],"For":[83],"example,":[84],"healthcare":[86,237],"applications,":[87],"each":[88,134,176,189],"client":[89,125,135],"(hospital)":[90],"gathers":[91],"patients":[94],"heterogeneous":[96],"demographics.":[97],"While":[98],"previous":[99],"works":[100],"have":[101],"made":[102],"efforts":[103],"address":[105],"by":[111],"assuming":[112],"various":[113,226],"relations":[114],"among":[115],"client-level":[116,222],"distributions":[118],"enabling":[120],"personalized":[121,173,211,223],"at":[123],"level,":[126],"they":[127],"ignore":[128],"internal":[130],"heterogeneity":[132],"within":[133],"require":[137],"explicit":[138],"domain":[140,200],"indicators,":[141],"hardly":[144],"accessible":[145],"data.":[148],"Here,":[149],"we":[150],"propose":[151],"Sample-Level":[152],"Prototypical":[153],"Federated":[154],"Learning":[155],"(SL-PFL)":[156],"bridge":[158],"gap.":[160],"SL-PFL":[161],"incorporates":[162],"prototypical":[163],"under":[165],"FL":[167,215],"framework":[168],"provides":[170],"fine-grained":[172],"model":[174,184,220],"for":[175,185],"sample":[178],"instead":[179],"one":[182],"uniform":[183],"all":[186],"samples":[187],"client.":[190],"Meanwhile,":[191],"can":[193],"be":[194],"trained":[195],"using":[196],"without":[198],"ground-truth":[199],"indicators.":[201],"Experimental":[202],"results":[203],"demonstrate":[204],"our":[206],"proposed":[207],"method":[208],"sample-level":[210],"outperforms":[213],"existing":[214],"methods":[216],"global":[219],"on":[225],"regression":[228],"classification":[230],"tasks":[231],"weather,":[233],"computer":[234],"vision,":[235],"applications.":[238]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}