{"id":"https://openalex.org/W7150929569","doi":"https://doi.org/10.48550/arxiv.2604.02990","title":"FedSQ: Optimized Weight Averaging via Fixed Gating","display_name":"FedSQ: Optimized Weight Averaging via Fixed Gating","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150929569","doi":"https://doi.org/10.48550/arxiv.2604.02990"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02990","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02990","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2604.02990","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124332705","display_name":"Cristian P\u00e9rez-Corral","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P\u00e9rez-Corral, Cristian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007319120","display_name":"Jose I. Mestre","orcid":"https://orcid.org/0000-0001-5954-4131"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mestre, Jose I.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133051853","display_name":"Alberto Fern\u00e1ndez-Hern\u00e1ndez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fern\u00e1ndez-Hern\u00e1ndez, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133052015","display_name":"Manuel F. Dolz","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dolz, Manuel F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014020713","display_name":"Jos\u00e9 Luis Duato","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duato, Jos\u00e9","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130468614","display_name":"Enrique S. Quintana-Ort\u00ed","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quintana-Ort\u00ed, Enrique S.","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":0.45410001277923584,"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":0.45410001277923584,"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":0.13040000200271606,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.06469999998807907,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/gating","display_name":"Gating","score":0.8359000086784363},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7296000123023987},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6054999828338623},{"id":"https://openalex.org/keywords/affine-transformation","display_name":"Affine transformation","score":0.5012000203132629},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.4830000102519989},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4666999876499176},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.44859999418258667},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4251999855041504}],"concepts":[{"id":"https://openalex.org/C194544171","wikidata":"https://www.wikidata.org/wiki/Q21105679","display_name":"Gating","level":2,"score":0.8359000086784363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7379999756813049},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7296000123023987},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6054999828338623},{"id":"https://openalex.org/C92757383","wikidata":"https://www.wikidata.org/wiki/Q382497","display_name":"Affine transformation","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.4830000102519989},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4666999876499176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45190000534057617},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.44859999418258667},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4251999855041504},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.39969998598098755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C2781020372","wikidata":"https://www.wikidata.org/wiki/Q533093","display_name":"On the fly","level":2,"score":0.3702999949455261},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.32190001010894775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.27079999446868896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26440000534057617},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02990","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02990","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02990","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02990","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.42805489897727966}],"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":[1,125],"(FL)":[2],"enables":[3],"collaborative":[4],"training":[5],"across":[6,119],"organizations":[7],"without":[8],"sharing":[9],"raw":[10],"data,":[11],"but":[12],"it":[13],"is":[14,37,114],"hindered":[15],"by":[16,23,53],"statistical":[17],"heterogeneity":[18],"(non-i.i.d.\\":[19],"client":[20,30],"data)":[21],"and":[22,46,117,145,153],"instability":[24],"of":[25,88,96],"naive":[26],"weight":[27],"averaging":[28],"under":[29,133,143],"drift.":[31],"In":[32],"many":[33],"cross-silo":[34],"deployments,":[35],"FL":[36],"warm-started":[38],"from":[39],"a":[40,77,84,93,111],"strong":[41],"pretrained":[42,98],"backbone":[43],"(e.g.,":[44],"ImageNet-1K)":[45],"then":[47],"adapted":[48],"to":[49,100,126,160],"local":[50],"domains.":[51],"Motivated":[52],"recent":[54],"evidence":[55],"that":[56,149],"ReLU-like":[57],"gating":[58,104,123],"regimes":[59],"(structural":[60],"knowledge)":[61],"stabilize":[62],"earlier":[63],"than":[64],"the":[65,97,122,167],"remaining":[66],"parameter":[67],"values":[68],"(quantitative":[69],"knowledge),":[70],"we":[71],"propose":[72],"FedSQ":[73,91,150],"(Federated":[74],"Structural-Quantitative":[75],"learning),":[76],"transfer-initialized":[78],"neural":[79,140],"federated":[80,107],"procedure":[81],"based":[82],"on":[83,137],"DualCopy,":[85],"piecewise-linear":[86],"view":[87],"deep":[89],"networks.":[90],"freezes":[92],"structural":[94],"copy":[95,113],"model":[99],"induce":[101],"fixed":[102],"binary":[103],"masks":[105],"during":[106],"fine-tuning,":[108],"while":[109,163],"only":[110],"quantitative":[112],"optimized":[115],"locally":[116],"aggregated":[118],"rounds.":[120],"Fixing":[121],"reduces":[124],"within-regime":[127],"affine":[128],"refinements,":[129],"which":[130],"stabilizes":[131],"aggregation":[132],"heterogeneous":[134],"partitions.":[135],"Experiments":[136],"two":[138],"convolutional":[139],"network":[141],"backbones":[142],"i.i.d.\\":[144],"Dirichlet":[146],"splits":[147],"show":[148],"improves":[151],"robustness":[152],"can":[154],"reduce":[155],"rounds-to-best":[156],"validation":[157],"performance":[158],"relative":[159],"standard":[161],"baselines":[162],"preserving":[164],"accuracy":[165],"in":[166],"transfer":[168],"setting.":[169]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}