{"id":"https://openalex.org/W4392904059","doi":"https://doi.org/10.1109/icassp48485.2024.10447454","title":"Towards Building The Federatedgpt: Federated Instruction Tuning","display_name":"Towards Building The Federatedgpt: Federated Instruction Tuning","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392904059","doi":"https://doi.org/10.1109/icassp48485.2024.10447454"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5101609539","display_name":"Jianyi Zhang","orcid":"https://orcid.org/0000-0001-8765-053X"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jianyi Zhang","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049755417","display_name":"Saeed Vahidian","orcid":"https://orcid.org/0000-0002-1258-0343"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Saeed Vahidian","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084250016","display_name":"Martin Kuo","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Martin Kuo","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107893340","display_name":"Chunyuan Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chunyuan Li","raw_affiliation_strings":["Microsoft Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research","institution_ids":["https://openalex.org/I4210164937"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101424484","display_name":"Ruiyi Zhang","orcid":"https://orcid.org/0000-0002-4776-6762"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ruiyi Zhang","raw_affiliation_strings":["Adobe Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe Research","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100751664","display_name":"Tong Yu","orcid":"https://orcid.org/0000-0002-9861-6270"},"institutions":[{"id":"https://openalex.org/I1306409833","display_name":"Adobe Systems (United States)","ror":"https://ror.org/059tvcg64","country_code":"US","type":"company","lineage":["https://openalex.org/I1306409833"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Yu","raw_affiliation_strings":["Adobe Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Adobe Research","institution_ids":["https://openalex.org/I1306409833"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031220156","display_name":"Guoyin Wang","orcid":"https://orcid.org/0000-0002-8521-5232"},"institutions":[{"id":"https://openalex.org/I4210089985","display_name":"Amazon (Germany)","ror":"https://ror.org/00b9ktm87","country_code":"DE","type":"company","lineage":["https://openalex.org/I1311688040","https://openalex.org/I4210089985"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Guoyin Wang","raw_affiliation_strings":["Amazon"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amazon","institution_ids":["https://openalex.org/I4210089985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058073627","display_name":"Yiran Chen","orcid":"https://orcid.org/0000-0002-1486-8412"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiran Chen","raw_affiliation_strings":["Duke University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Duke University","institution_ids":["https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101609539"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":22.6576,"has_fulltext":false,"cited_by_count":76,"citation_normalized_percentile":{"value":0.99613907,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"6915","last_page":"6919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.996399998664856,"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":0.996399998664856,"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/T10028","display_name":"Topic Modeling","score":0.9839000105857849,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.9606999754905701,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.719021737575531},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6899820566177368},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.5778990387916565},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3333970308303833},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.24817940592765808},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.101016104221344}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.719021737575531},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6899820566177368},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.5778990387916565},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3333970308303833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.24817940592765808},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.101016104221344},{"id":"https://openalex.org/C138496976","wikidata":"https://www.wikidata.org/wiki/Q175002","display_name":"Developmental psychology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447454","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447454","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6624552157","display_name":null,"funder_award_id":"W911NF-23-2-0224","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7512341244","display_name":null,"funder_award_id":"2332744","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G8377316191","display_name":null,"funder_award_id":"2112562","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320309133","display_name":"Duke University","ror":"https://ror.org/00py81415"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W2612193523","https://openalex.org/W2896457183","https://openalex.org/W2923014074","https://openalex.org/W2962922117","https://openalex.org/W3168867926","https://openalex.org/W3196731672","https://openalex.org/W3206389158","https://openalex.org/W4221152824","https://openalex.org/W4226278401","https://openalex.org/W4280616980","https://openalex.org/W4286987939","https://openalex.org/W4292779060","https://openalex.org/W4318619660","https://openalex.org/W4362707064","https://openalex.org/W4385572634","https://openalex.org/W6728757088","https://openalex.org/W6755207826","https://openalex.org/W6778883912","https://openalex.org/W6796581206","https://openalex.org/W6800875267","https://openalex.org/W6810738896","https://openalex.org/W6838080713","https://openalex.org/W6851960618"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2118717649","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W2035068594","https://openalex.org/W4225593417","https://openalex.org/W2573498121"],"abstract_inverted_index":{"While":[0],"\"instruction-tuned\"":[1],"generative":[2],"large":[3,23],"language":[4],"models":[5],"(LLMs)":[6],"have":[7],"demonstrated":[8],"an":[9],"impressive":[10],"ability":[11],"to":[12,14,44,61,67,149,157,167,173,188,211],"generalize":[13],"new":[15,89],"tasks,":[16],"the":[17,71,102,106,113,151,189,193,207,218,227],"training":[18,233],"phases":[19],"heavily":[20],"rely":[21],"on":[22,181,226],"amounts":[24,139],"of":[25,54,73,109,116,140,153,195,220],"diverse":[26,178,223],"and":[27,34,56,78,160,169,192],"high-quality":[28,38],"instruction":[29,107,118,224],"data":[30,128,190,196],"(such":[31],"as":[32,101],"ChatGPT":[33],"GPT-4).":[35],"Unfortunately,":[36],"acquiring":[37],"data,":[39,46,69],"especially":[40,124],"when":[41],"it":[42,75,164],"comes":[43],"human-written":[45],"can":[47,63,144],"pose":[48],"significant":[49],"challenges":[50],"both":[51],"in":[52],"terms":[53],"cost":[55,194],"accessibility.":[57],"Moreover,":[58],"concerns":[59,186],"related":[60,187],"privacy":[62],"further":[64],"limit":[65],"access":[66],"such":[68],"making":[70],"process":[72],"obtaining":[74],"a":[76,88,146],"complex":[77],"nuanced":[79],"undertaking.":[80],"To":[81],"tackle":[82],"this":[83,199],"issue,":[84],"our":[85,214],"study":[86],"introduces":[87],"approach":[90,148],"called":[91],"Federated":[92],"Instruction":[93],"Tuning":[94],"(FedIT),":[95],"which":[96],"leverages":[97],"federated":[98],"learning":[99,103],"(FL)":[100],"framework":[104,216],"for":[105,120],"tuning":[108,119],"LLMs.":[110,121,221],"This":[111,122],"marks":[112],"first":[114],"exploration":[115],"FL-based":[117],"is":[123,129,165],"important":[125],"since":[126],"text":[127],"predominantly":[130],"generated":[131],"by":[132],"end":[133],"users.":[134],"For":[135],"example,":[136],"collecting":[137],"extensive":[138,203],"everyday":[141],"user":[142],"conversations":[143],"be":[145],"useful":[147],"improving":[150],"generalizability":[152],"LLMs,":[154],"allowing":[155],"them":[156],"generate":[158],"authentic":[159],"natural":[161],"responses.":[162],"Therefore,":[163],"imperative":[166],"design":[168],"adapt":[170],"FL":[171],"approaches":[172],"effectively":[174],"leverage":[175,202],"these":[176],"users\u2019":[177],"instructions":[179],"stored":[180],"local":[182,237],"devices":[183],"while":[184],"mitigating":[185],"sensitivity":[191],"transmission.":[197],"In":[198],"study,":[200],"we":[201],"qualitative":[204],"analysis,":[205],"including":[206],"prevalent":[208],"GPT-4":[209],"auto-evaluation":[210],"illustrate":[212],"how":[213],"FedIT":[215,230],"enhances":[217],"performance":[219],"Utilizing":[222],"sets":[225],"client":[228],"side,":[229],"outperforms":[231],"centralized":[232],"with":[234],"only":[235],"limited":[236],"instructions.":[238]},"counts_by_year":[{"year":2026,"cited_by_count":13},{"year":2025,"cited_by_count":45},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":4}],"updated_date":"2026-05-23T08:51:43.019350","created_date":"2025-10-10T00:00:00"}