{"id":"https://openalex.org/W7160660319","doi":"https://doi.org/10.48550/arxiv.2605.06446","title":"FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing","display_name":"FedFrozen: Two-Stage Federated Optimization via Attention Kernel Freezing","publication_year":2026,"publication_date":"2026-05-07","ids":{"openalex":"https://openalex.org/W7160660319","doi":"https://doi.org/10.48550/arxiv.2605.06446"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.06446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.06446","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5009276209","display_name":"Junye Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Junye","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135698417","display_name":"Zhenghao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhenghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135708169","display_name":"Yushi Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yushi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135690694","display_name":"Long Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Long","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":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":0.6783000230789185,"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":0.6783000230789185,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.05820000171661377,"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":0.04360000044107437,"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/optimization-problem","display_name":"Optimization problem","score":0.48019999265670776},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4438000023365021},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.43209999799728394},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.4242999851703644},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.4140999913215637},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4004000127315521},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3379000127315521}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8093000054359436},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.48019999265670776},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4438000023365021},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.43209999799728394},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.4242999851703644},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.4140999913215637},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3379000127315521},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3303999900817871},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C2987595161","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Optimization algorithm","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3050999939441681},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2775000035762787},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.272599995136261},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2712000012397766},{"id":"https://openalex.org/C3018263672","wikidata":"https://www.wikidata.org/wiki/Q1296251","display_name":"Efficient algorithm","level":2,"score":0.26919999718666077},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2574999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.06446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.06446","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.06446","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"},"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],"learning":[1],"with":[2],"heterogeneous":[3,52,205],"clients":[4],"remains":[5],"a":[6,79,88,106,131,149,159,165],"significant":[7],"challenge":[8],"for":[9],"deep":[10],"learning,":[11],"primarily":[12],"due":[13],"to":[14,125,158],"client":[15],"drift":[16],"arising":[17],"from":[18],"inconsistent":[19],"local":[20],"updates.":[21],"Existing":[22],"federated":[23,53,70,108,206],"optimization":[24,109,162],"methods":[25],"typically":[26],"address":[27],"this":[28,57,101],"issue":[29],"through":[30],"objective-level":[31],"regularization":[32],"or":[33],"update-correction":[34],"mechanisms.":[35],"Recent":[36],"studies,":[37],"however,":[38],"suggest":[39],"that":[40,111,136,176,193],"Transformer-based":[41],"architectures":[42],"may":[43],"be":[44,141],"inherently":[45],"more":[46],"robust":[47],"than":[48],"conventional":[49],"models":[50,203],"under":[51,95,164],"training.":[54],"Motivated":[55],"by":[56],"observation,":[58],"we":[59,73,103,134],"investigate":[60],"how":[61],"different":[62],"parameter":[63],"components":[64],"within":[65],"the":[66,75,84,96,120,127,137,154,178,185,197],"attention":[67,76,85,167],"mechanism":[68],"influence":[69],"optimization.":[71],"Specifically,":[72],"decompose":[74],"module":[77],"into":[78],"query/key":[80,121],"block,":[81,90],"which":[82,91],"determines":[83],"kernel,":[86],"and":[87,117,189,199],"value":[89,128],"performs":[92,113],"semantic":[93],"transformation":[94],"induced":[97],"kernel.":[98,168],"Based":[99],"on":[100,148],"perspective,":[102],"propose":[104],"FedFrozen,":[105],"two-stage":[107],"framework":[110],"first":[112],"full-model":[114],"warm-up":[115,138,181],"training":[116],"then":[118],"freezes":[119],"block":[122],"while":[123,153],"continuing":[124],"optimize":[126],"block.":[129],"Under":[130],"linear-attention":[132],"formulation,":[133],"show":[135],"stage":[139,156],"can":[140],"interpreted":[142],"as":[143],"an":[144,173],"inexact":[145],"descent":[146],"procedure":[147],"regularized":[150],"kernel-profile":[151],"objective,":[152],"frozen":[155],"reduces":[157],"restricted":[160],"value-block":[161],"problem":[163],"fixed":[166],"Our":[169],"analysis":[170],"further":[171],"reveals":[172],"explicit":[174],"trade-off":[175],"governs":[177],"choice":[179],"of":[180,201],"length.":[182],"Simulations":[183],"validate":[184],"predicted":[186],"bias-drift":[187],"behavior,":[188],"real-data":[190],"experiments":[191],"demonstrate":[192],"FedFrozen":[194],"improves":[195],"both":[196],"stability":[198],"effectiveness":[200],"Transformer":[202],"in":[204],"learning.":[207]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-09T00:00:00"}