{"id":"https://openalex.org/W4406458349","doi":"https://doi.org/10.1109/bigdata62323.2024.10825712","title":"Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning","display_name":"Exploring Parameter-Efficient Fine-Tuning to Enable Foundation Models in Federated Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406458349","doi":"https://doi.org/10.1109/bigdata62323.2024.10825712"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101850376","display_name":"Guangyu Sun","orcid":"https://orcid.org/0000-0001-6436-0820"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guangyu Sun","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101570322","display_name":"Umar Khalid","orcid":"https://orcid.org/0000-0002-3357-9720"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Umar Khalid","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031727113","display_name":"Mat\u00edas Mendieta","orcid":"https://orcid.org/0000-0002-5497-6207"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Matias Mendieta","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100383534","display_name":"Pu Wang","orcid":"https://orcid.org/0000-0003-1988-5016"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Pu Wang","raw_affiliation_strings":["University of North Carolina at Charlotte,Department of Computer Science,Charlotte,NC,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of North Carolina at Charlotte,Department of Computer Science,Charlotte,NC,USA","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114973667","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0001-7359-8515"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chen Chen","raw_affiliation_strings":["University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Central Florida,Center for Research in Computer Vision,Orlando,FL,USA","institution_ids":["https://openalex.org/I106165777"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1336,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90185009,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"8015","last_page":"8024"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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":0.9980000257492065,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9970999956130981,"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/foundation","display_name":"Foundation (evidence)","score":0.7526581287384033},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7404124736785889},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47142452001571655},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.41429635882377625}],"concepts":[{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.7526581287384033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404124736785889},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47142452001571655},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41429635882377625},{"id":"https://openalex.org/C95457728","wikidata":"https://www.wikidata.org/wiki/Q309","display_name":"History","level":0,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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":94,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1583837637","https://openalex.org/W2109426455","https://openalex.org/W2126579184","https://openalex.org/W2194775991","https://openalex.org/W2535838896","https://openalex.org/W2592962403","https://openalex.org/W2619947201","https://openalex.org/W2734358244","https://openalex.org/W2806857275","https://openalex.org/W2883780447","https://openalex.org/W2950346378","https://openalex.org/W2951213900","https://openalex.org/W2963795951","https://openalex.org/W2964881778","https://openalex.org/W3034572008","https://openalex.org/W3038022836","https://openalex.org/W3043723611","https://openalex.org/W3094502228","https://openalex.org/W3099793224","https://openalex.org/W3102834148","https://openalex.org/W3113151582","https://openalex.org/W3118608800","https://openalex.org/W3126083338","https://openalex.org/W3126721948","https://openalex.org/W3128632573","https://openalex.org/W3153675281","https://openalex.org/W3168867926","https://openalex.org/W3174770825","https://openalex.org/W3176828726","https://openalex.org/W3182158470","https://openalex.org/W3195577433","https://openalex.org/W3204250462","https://openalex.org/W3205270560","https://openalex.org/W3205949070","https://openalex.org/W3213321731","https://openalex.org/W3213758553","https://openalex.org/W3213815372","https://openalex.org/W4205228770","https://openalex.org/W4206320562","https://openalex.org/W4221145545","https://openalex.org/W4221167396","https://openalex.org/W4280534475","https://openalex.org/W4281838252","https://openalex.org/W4282005462","https://openalex.org/W4283455321","https://openalex.org/W4283718397","https://openalex.org/W4285595687","https://openalex.org/W4287203292","https://openalex.org/W4297687186","https://openalex.org/W4306294758","https://openalex.org/W4309208182","https://openalex.org/W4312651322","https://openalex.org/W4312680444","https://openalex.org/W4312705808","https://openalex.org/W4312950667","https://openalex.org/W4318619660","https://openalex.org/W4319453767","https://openalex.org/W4372260089","https://openalex.org/W4385572063","https://openalex.org/W4390871722","https://openalex.org/W4392576654","https://openalex.org/W4402916309","https://openalex.org/W6600983433","https://openalex.org/W6728757088","https://openalex.org/W6728975057","https://openalex.org/W6759238902","https://openalex.org/W6763048141","https://openalex.org/W6773817997","https://openalex.org/W6779308105","https://openalex.org/W6781318954","https://openalex.org/W6784333009","https://openalex.org/W6785440099","https://openalex.org/W6787972765","https://openalex.org/W6794559225","https://openalex.org/W6796581206","https://openalex.org/W6797927333","https://openalex.org/W6800751262","https://openalex.org/W6802347785","https://openalex.org/W6802386650","https://openalex.org/W6802744804","https://openalex.org/W6803951002","https://openalex.org/W6809665334","https://openalex.org/W6810265253","https://openalex.org/W6837789219","https://openalex.org/W6838701581","https://openalex.org/W6838751721","https://openalex.org/W6838865580","https://openalex.org/W6839670772","https://openalex.org/W6846313647","https://openalex.org/W6846725466","https://openalex.org/W6849878650","https://openalex.org/W6861790787","https://openalex.org/W6955071965"],"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":[1,62,149,214],"(FL)":[2],"has":[3,54],"emerged":[4],"as":[5,82],"a":[6,97,114,153,166,184,209,219],"promising":[7],"paradigm":[8,30,221],"for":[9,222],"enabling":[10],"the":[11,20,27,40,48,83,90,102,134,142,161,188,194],"collaborative":[12],"training":[13],"of":[14,50,144,163,168,187,212],"models":[15,53,71,108,124],"without":[16],"centralized":[17],"access":[18],"to":[19,37,44,57,116,127],"raw":[21],"data":[22,171],"on":[23,101],"local":[24],"devices.":[25],"In":[26,86],"typical":[28],"FL":[29,126],"(e.g.,":[31],"FedAvg),":[32],"model":[33,92,189],"weights":[34,93],"are":[35,72,109],"sent":[36],"and":[38,64,120,150,173,181,224],"from":[39],"server":[41],"each":[42],"round":[43],"participating":[45],"clients.":[46],"Recently,":[47],"use":[49,143],"small":[51,185],"pre-trained":[52,70,123],"been":[55],"shown":[56],"be":[58,199],"effective":[59,225],"in":[60,125,147,193,208],"federated":[61,148,213,226],"optimization":[63],"improving":[65],"convergence.":[66],"However,":[67],"recent":[68],"state-of-the-art":[69],"getting":[73],"more":[74,79,106],"capable":[75,107],"but":[76],"also":[77],"have":[78],"parameters,":[80],"known":[81],"\"Foundation":[84],"Models.\"":[85],"conventional":[87],"FL,":[88],"sharing":[89,183],"enormous":[91],"can":[94,198],"quickly":[95],"put":[96],"massive":[98],"communication":[99,135,196],"burden":[100],"system,":[103],"especially":[104],"if":[105],"employed.":[110],"Can":[111],"we":[112,140,158],"find":[113],"solution":[115],"enable":[117],"those":[118],"strong":[119],"readily":[121],"available":[122],"achieve":[128],"excellent":[129],"performance":[130,162,207],"while":[131,201],"simultaneously":[132],"reducing":[133],"burden?":[136],"To":[137],"this":[138],"end,":[139],"investigate":[141],"parameter-efficient":[145],"fine-tuning":[146],"thus":[151],"introduce":[152],"new":[154,220],"framework:":[155],"FedPEFT.":[156],"Specifically,":[157],"systemically":[159],"evaluate":[160],"FedPEFT":[164],"across":[165],"variety":[167],"client":[169],"stability,":[170],"distribution,":[172],"differential":[174],"privacy":[175],"settings.":[176],"By":[177],"only":[178],"locally":[179],"tuning":[180],"globally":[182],"portion":[186],"weights,":[190],"significant":[191],"reductions":[192],"total":[195],"overhead":[197],"achieved":[200],"maintaining":[202],"competitive":[203],"or":[204],"even":[205],"better":[206],"wide":[210],"range":[211],"scenarios,":[215],"providing":[216],"insight":[217],"into":[218],"practical":[223],"systems.":[227]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}