{"id":"https://openalex.org/W4286542654","doi":"https://doi.org/10.1109/tnnls.2022.3190359","title":"Personalized Federated Few-Shot Learning","display_name":"Personalized Federated Few-Shot Learning","publication_year":2022,"publication_date":"2022-07-21","ids":{"openalex":"https://openalex.org/W4286542654","doi":"https://doi.org/10.1109/tnnls.2022.3190359"},"language":"en","primary_location":{"id":"doi:10.1109/tnnls.2022.3190359","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190359","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-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/A5046842314","display_name":"Yunfeng Zhao","orcid":"https://orcid.org/0000-0001-9493-168X"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yunfeng Zhao","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027082940","display_name":"Guoxian Yu","orcid":"https://orcid.org/0000-0002-1667-6705"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxian Yu","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100384767","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-5890-0365"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090148962","display_name":"Carlotta Domeniconi","orcid":"https://orcid.org/0000-0003-2140-9596"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Carlotta Domeniconi","raw_affiliation_strings":["Department of Computer Science, George Mason University, Fairfax, VA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, George Mason University, Fairfax, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079800756","display_name":"Maozu Guo","orcid":"https://orcid.org/0000-0001-6228-6276"},"institutions":[{"id":"https://openalex.org/I62853816","display_name":"Beijing University of Civil Engineering and Architecture","ror":"https://ror.org/02yj0p855","country_code":"CN","type":"education","lineage":["https://openalex.org/I62853816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maozu Guo","raw_affiliation_strings":["Department of Computer Science, Beijing University of Civil Engineering and Architecture, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Beijing University of Civil Engineering and Architecture, Beijing, China","institution_ids":["https://openalex.org/I62853816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000755750","display_name":"Xiangliang Zhang","orcid":"https://orcid.org/0000-0002-3574-5665"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiangliang Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101414718","display_name":"Lizhen Cui","orcid":"https://orcid.org/0000-0002-8262-8883"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lizhen Cui","raw_affiliation_strings":["School of Software, Shandong University, Jinan, China"],"affiliations":[{"raw_affiliation_string":"School of Software, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5046842314"],"corresponding_institution_ids":["https://openalex.org/I154099455"],"apc_list":null,"apc_paid":null,"fwci":5.1702,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.96030545,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"35","issue":"2","first_page":"2534","last_page":"2544"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9988999962806702,"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":0.9988999962806702,"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":0.9921000003814697,"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":0.8349259495735168},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.7681616544723511},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7489568591117859},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7062339782714844},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6603544354438782},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5401737689971924},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.5323237776756287},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.46878159046173096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4285290539264679},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41454654932022095},{"id":"https://openalex.org/keywords/labeled-data","display_name":"Labeled data","score":0.4108414649963379}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8349259495735168},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7681616544723511},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7489568591117859},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7062339782714844},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6603544354438782},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5401737689971924},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.5323237776756287},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.46878159046173096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4285290539264679},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41454654932022095},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.4108414649963379},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tnnls.2022.3190359","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tnnls.2022.3190359","pdf_url":null,"source":{"id":"https://openalex.org/S4210175523","display_name":"IEEE Transactions on Neural Networks and Learning Systems","issn_l":"2162-237X","issn":["2162-237X","2162-2388"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["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 Neural Networks and Learning Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7200000286102295}],"awards":[{"id":"https://openalex.org/G1333446516","display_name":null,"funder_award_id":"91846205","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5564147583","display_name":null,"funder_award_id":"61872300","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6836859050","display_name":null,"funder_award_id":"62031003","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"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":58,"referenced_works":["https://openalex.org/W2112796928","https://openalex.org/W2168973828","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2807006176","https://openalex.org/W2896457183","https://openalex.org/W2912213068","https://openalex.org/W2930926105","https://openalex.org/W2954070046","https://openalex.org/W2962722184","https://openalex.org/W2962837320","https://openalex.org/W2962876782","https://openalex.org/W2989289980","https://openalex.org/W2990789643","https://openalex.org/W2996976377","https://openalex.org/W3012328518","https://openalex.org/W3014624619","https://openalex.org/W3027472889","https://openalex.org/W3032684551","https://openalex.org/W3034312118","https://openalex.org/W3034942609","https://openalex.org/W3047304572","https://openalex.org/W3047669212","https://openalex.org/W3080934299","https://openalex.org/W3091635927","https://openalex.org/W3092053846","https://openalex.org/W3094542121","https://openalex.org/W3103245149","https://openalex.org/W3112044954","https://openalex.org/W3118608800","https://openalex.org/W3121442875","https://openalex.org/W3133814152","https://openalex.org/W3154934604","https://openalex.org/W3190576293","https://openalex.org/W3193589553","https://openalex.org/W3198563652","https://openalex.org/W3202201804","https://openalex.org/W4206731046","https://openalex.org/W4206878041","https://openalex.org/W4288112736","https://openalex.org/W6717697761","https://openalex.org/W6728757088","https://openalex.org/W6735236233","https://openalex.org/W6736057607","https://openalex.org/W6738383168","https://openalex.org/W6743661861","https://openalex.org/W6751281049","https://openalex.org/W6752029299","https://openalex.org/W6757139170","https://openalex.org/W6759238902","https://openalex.org/W6767926897","https://openalex.org/W6770590064","https://openalex.org/W6770634426","https://openalex.org/W6779174293","https://openalex.org/W6783533280","https://openalex.org/W6784336702","https://openalex.org/W6786364885","https://openalex.org/W6786597537"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W156213964","https://openalex.org/W2050960118","https://openalex.org/W1757117718","https://openalex.org/W3172418599","https://openalex.org/W4299840061","https://openalex.org/W4315629742"],"abstract_inverted_index":{"Personalized":[0],"federated":[1,122],"learning":[2,78,124],"(PFL)":[3],"learns":[4,132],"a":[5,12,59,69,108,118,133],"personalized":[6,49,121,134],"model":[7],"for":[8,73,139,169],"each":[9,16,140,180],"client":[10,17,106,141],"in":[11,58,94],"decentralized":[13,95],"manner,":[14],"where":[15,62],"owns":[18],"private":[19],"data":[20,26,154],"that":[21,40,206],"are":[22,29,91],"not":[23,92],"shared":[24],"and":[25,31,135,160,163,191],"among":[27],"clients":[28,41,66,156,165],"non-independent":[30],"identically":[32],"distributed":[33],"(i.i.d.)":[34],"However,":[35],"existing":[36,52,76],"PFL":[37,53,100],"solutions":[38,54],"assume":[39],"have":[42,68],"sufficient":[43],"training":[44,84,103],"samples":[45,72,104,186,195],"to":[46,98,126,157,185],"jointly":[47],"induce":[48],"models.":[50],"Thus,":[51],"cannot":[55],"perform":[56,146],"well":[57,147],"few-shot":[60,77,123],"scenario,":[61],"most":[63],"or":[64],"all":[65],"only":[67],"handful":[70],"of":[71,155,187,196],"training.":[74],"Furthermore,":[75],"(FSL)":[79],"approaches":[80],"typically":[81],"need":[82],"centralized":[83],"data;":[85],"as":[86],"such,":[87],"these":[88],"FSL":[89],"methods":[90],"applicable":[93],"scenarios.":[96],"How":[97],"enable":[99],"with":[101,171],"limited":[102],"per":[105],"is":[107,182],"practical":[109],"but":[110],"understudied":[111],"problem.":[112,129],"In":[113,175],"this":[114,128],"article,":[115],"we":[116],"propose":[117],"solution":[119],"called":[120],"(pFedFSL)":[125],"tackle":[127],"Specifically,":[130],"pFedFSL":[131,207],"discriminative":[136],"feature":[137,178],"space":[138],"by":[142],"identifying":[143],"which":[144,149,164],"models":[145],"on":[148,201],"clients,":[150,162],"without":[151],"exposing":[152],"local":[153],"the":[158,172,176,188],"server":[159],"other":[161],"should":[166],"be":[167],"selected":[168],"collaboration":[170],"target":[173],"client.":[174],"learned":[177],"spaces,":[179],"sample":[181],"made":[183],"closer":[184],"same":[189],"category":[190],"farther":[192],"away":[193],"from":[194],"different":[197,212],"categories.":[198],"Experimental":[199],"results":[200],"four":[202],"benchmark":[203],"datasets":[204],"demonstrate":[205],"outperforms":[208],"competitive":[209],"baselines":[210],"across":[211],"settings.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":23},{"year":2024,"cited_by_count":10},{"year":2023,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2022-07-22T00:00:00"}