{"id":"https://openalex.org/W7158755176","doi":"https://doi.org/10.48550/arxiv.2604.26388","title":"SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning","display_name":"SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7158755176","doi":"https://doi.org/10.48550/arxiv.2604.26388"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.26388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26388","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.26388","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134920332","display_name":"Yimeng Shan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shan, Yimeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134921816","display_name":"Zhaorui Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhaorui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134915940","display_name":"Sheng Di","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di, Sheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134918832","display_name":"Yu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067748041","display_name":"Xiaoyi Lu","orcid":"https://orcid.org/0000-0001-7581-8905"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Xiaoyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134884594","display_name":"Benben Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Benben","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.9164000153541565,"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":0.9164000153541565,"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/T11719","display_name":"Data Quality and Management","score":0.01510000042617321,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.007699999958276749,"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":0.7178000211715698},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5899999737739563},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47589999437332153},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4140999913215637},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.41019999980926514},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4023999869823456},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.37040001153945923},{"id":"https://openalex.org/keywords/work","display_name":"Work (physics)","score":0.34869998693466187},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.3287000060081482}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.843999981880188},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.7178000211715698},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5899999737739563},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.43790000677108765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.421999990940094},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4140999913215637},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4023999869823456},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3894999921321869},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.37040001153945923},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C206345919","wikidata":"https://www.wikidata.org/wiki/Q20380951","display_name":"Resource (disambiguation)","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C86037889","wikidata":"https://www.wikidata.org/wiki/Q4330127","display_name":"Learning to rank","level":3,"score":0.29600000381469727},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2842000126838684},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2780609101","wikidata":"https://www.wikidata.org/wiki/Q17156588","display_name":"Resource management (computing)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25429999828338623}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.26388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26388","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.26388","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.26388","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":"article"},"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":{"Federated":[0],"Split":[1],"Learning":[2],"has":[3],"been":[4],"identified":[5],"as":[6],"an":[7,104],"efficient":[8],"approach":[9,168,184,188],"to":[10,61,93,117,123,134,141,148,169],"address":[11,62,94],"the":[12,55,63,71,79,83,136,143,150,171,186],"computational":[13],"resource":[14],"constraints":[15],"of":[16],"clients":[17,60,116],"in":[18,139,153],"classical":[19],"federated":[20,106,160],"learning,":[21],"while":[22],"guaranteeing":[23],"data":[24,31,64,152,173],"privacy":[25],"for":[26,49,110,156,189],"distributed":[27],"model":[28,129,194],"training":[29,42,172],"across":[30,58],"owners.":[32],"However,":[33],"it":[34],"faces":[35],"some":[36],"critical":[37],"challenges":[38,52],"when":[39],"such":[40],"a":[41,165],"strategy":[43],"meets":[44],"large":[45],"language":[46],"models":[47],"(LLMs)":[48],"fine-tuning.":[50,112],"Such":[51],"include":[53],"setting":[54],"cutlayer":[56,140],"adaptively":[57],"different":[59,115,119,175],"and":[65,127,193],"device":[66],"heterogeneity":[67],"issues,":[68],"which":[69],"affect":[70],"system":[72,109],"performance":[73,195],"significantly.":[74],"In":[75,146],"addition,":[76],"efficiently":[77],"reducing":[78],"communication":[80,144],"overhead":[81],"during":[82],"fine-tuning":[84,190],"procedure":[85],"is":[86],"also":[87,132],"another":[88],"challenge.":[89],"No":[90],"work":[91],"tries":[92],"these":[95],"challenges.":[96],"To":[97],"bridge":[98],"this":[99],"gap,":[100],"we":[101,163],"propose":[102,164],"SplitTF,":[103],"adaptive":[105],"split":[107,159],"learning":[108,161],"LLMs":[111],"SplitFT":[113,131],"enables":[114],"set":[118],"cut":[120],"layers":[121],"according":[122],"their":[124],"computation":[125],"resources":[126],"trained":[128],"performance.":[130],"proposes":[133],"reduce":[135,142],"LoRA":[137],"rank":[138],"overhead.":[145],"addition":[147],"simulating":[149],"heterogeneous":[151],"real-world":[154],"applications":[155],"our":[157,182],"proposed":[158,183],"system,":[162],"length-based":[166],"Dirichlet":[167],"divide":[170],"into":[174],"clients.":[176],"Extensive":[177],"experimental":[178],"results":[179],"show":[180],"that":[181],"outperforms":[185],"state-of-the-art":[187],"time":[191],"efficiency":[192],"based":[196],"on":[197],"various":[198],"popular":[199],"benchmarks.":[200]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-01T00:00:00"}