{"id":"https://openalex.org/W7140085529","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.227","title":"FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation","display_name":"FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7140085529","doi":"https://doi.org/10.18653/v1/2026.findings-eacl.227"},"language":null,"primary_location":{"id":"doi:10.18653/v1/2026.findings-eacl.227","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.227","pdf_url":"https://aclanthology.org/2026.findings-eacl.227.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.findings-eacl.227.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130358683","display_name":"Fatema Siddika","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fatema Siddika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080882994","display_name":"Md. Anwar Hossen","orcid":"https://orcid.org/0000-0002-5933-9880"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Md Anwar Hossen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032589750","display_name":"J. Pablo Mu\u00f1oz","orcid":"https://orcid.org/0000-0002-5901-4023"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Juan Pablo Munoz","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130340412","display_name":"Tanya G. Roosta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tanya G. Roosta","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044574038","display_name":"Anuj K. Sharma","orcid":"https://orcid.org/0000-0003-4899-7113"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anuj Sharma","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130340449","display_name":"Ali Jannesari","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ali Jannesari","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24265879,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4341","last_page":"4362"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10237","display_name":"Cryptography and Data Security","score":0.09700000286102295,"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/T10237","display_name":"Cryptography and Data Security","score":0.09700000286102295,"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/T10951","display_name":"Cryptographic Implementations and Security","score":0.09059999883174896,"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/T12122","display_name":"Physical Unclonable Functions (PUFs) and Hardware Security","score":0.08160000294446945,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/representation","display_name":"Representation (politics)","score":0.5257999897003174},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.2994999885559082},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.2858000099658966},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.27160000801086426},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.2667999863624573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6495000123977661},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5257999897003174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3693999946117401},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.36059999465942383},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.257999986410141},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2483000010251999}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/2026.findings-eacl.227","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.227","pdf_url":"https://aclanthology.org/2026.findings-eacl.227.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/2026.findings-eacl.227","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.findings-eacl.227","pdf_url":"https://aclanthology.org/2026.findings-eacl.227.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Findings of the Association for Computational Linguistics: EACL 2026","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.478724867105484}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7140085529.pdf","grobid_xml":"https://content.openalex.org/works/W7140085529.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Parameter-efficient":[0],"fine-tuning":[1,26,101],"(PEFT)":[2],"adapts":[3],"large":[4],"pre-trained":[5],"models":[6],"by":[7,152,166],"updating":[8,29],"only":[9],"a":[10,78,96],"small":[11],"subset":[12],"of":[13,141],"parameters.Recently,":[14],"Representation":[15,75],"Fine-Tuning":[16,76],"(ReFT)":[17],"has":[18],"emerged":[19],"as":[20],"an":[21,161],"effective":[22],"alternative.ReFT":[23],"shifts":[24],"the":[25,138],"paradigm":[27],"from":[28],"model":[30,64],"weights":[31],"to":[32,58,81,90,112,170,197],"directly":[33],"manipulating":[34],"hidden":[35,84,92],"representations":[36,93],"that":[37],"capture":[38],"rich":[39,100],"semantic":[40,124],"information,":[41],"and":[42,66,98,143,149,173,187],"outperform":[43],"state-of-the-art":[44,180],"PEFTs":[45],"in":[46,51,60],"standalone":[47],"settings.However,":[48],"its":[49],"application":[50],"Federated":[52,74],"Learning":[53],"(FL)":[54],"remains":[55],"challenging":[56],"due":[57],"heterogeneity":[59],"clients'":[61,83],"data":[62],"distributions,":[63],"capacities,":[65],"computational":[67],"resources.To":[68],"address":[69],"these":[70],"challenges,":[71],"we":[72,129],"introduce":[73],"(FedReFT),":[77],"novel":[79],"approach":[80],"fine-tune":[82],"representations.Fe-dReFT":[85],"applies":[86],"sparse":[87],"intervention":[88],"layers":[89],"steer":[91],"directly,":[94],"offering":[95],"lightweight":[97],"semantically":[99],"alternative":[102],"ideal":[103],"for":[104],"edge":[105],"devices.However,":[106],"representation-level":[107],"updates":[108,140],"are":[109],"especially":[110],"vulnerable":[111],"aggregation":[113],"mismatch":[114],"under":[115,176],"different":[116],"task":[117],"heterogeneity,":[118],"where":[119,134],"naive":[120],"averaging":[121],"can":[122],"corrupt":[123],"alignment.To":[125],"mitigate":[126],"this":[127],"issue,":[128],"propose":[130],"All-But-Me":[131],"(ABM)":[132],"aggregation,":[133],"each":[135],"client":[136],"receives":[137],"aggregated":[139],"others":[142],"partially":[144],"incorporates":[145],"them,":[146],"enabling":[147],"stable":[148],"personalized":[150],"learning":[151],"balancing":[153],"local":[154,172],"focus":[155],"with":[156],"global":[157,174],"knowledge.We":[158],"further":[159],"design":[160],"adaptive":[162],"update":[163],"strategy":[164],"inspired":[165],"Test-Time":[167],"Computing":[168],"(TTC)":[169],"balance":[171],"contributions":[175],"heterogeneous":[177],"conditions.Fe-dReFT":[178],"achieves":[179],"performance":[181],"on":[182],"commonsense":[183],"reasoning,":[184,186],"arithmetic":[185],"GLUE":[188,280],"benchmarks,":[189],"while":[190],"delivering":[191],"1-49":[192],"higher":[193],"parameter":[194],"efficiency":[195],"compared":[196],"leading":[198],"LoRA-based":[199],"methods.The":[200],"paper":[201],"code":[202],"is":[203],"available":[204],"at":[205],"Anonymous":[206],"Repository":[207],"*":[208],"Equal":[209],"contribution":[210],"0":[211,215,235,239,246,264,268],".0":[212,236,265],"1":[213,217,237,241,243,245,266,270],"%":[214,218,238,242,244,247,267,271],".":[216,240,269],"Trainable":[219,248,272],"Param":[220,249,273],"(%)":[221,230,250,274],"45":[222],"46":[223],"47":[224],"48":[225],"49":[226],"50":[227],"Avg":[228],"Accu":[229],"Arithmetic":[231],"FedReFT(r=8)":[232,258],"FFA-LoRA":[233,261,282],"FedSA-LoRA":[234,283],"70":[251],"72":[252],"74":[253],"76":[254],"78":[255],"80":[256],"Commonsense":[257],"Fed-SB":[259],"FedReFT(r=32)":[260],"FedIT":[262],"FLoRA":[263],"88":[275],"89":[276],"90":[277],"91":[278],"92":[279],"FedReFT(r=1)":[281],"FedDPA-LoRA":[284]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-24T00:00:00"}