{"id":"https://openalex.org/W7160948625","doi":"https://doi.org/10.48550/arxiv.2605.10748","title":"Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs","display_name":"Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160948625","doi":"https://doi.org/10.48550/arxiv.2605.10748"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.10748","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10748","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.2605.10748","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135979288","display_name":"Li Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135985205","display_name":"Xiaolei Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Xiaolei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135982868","display_name":"Qinglun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Qinglun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135939588","display_name":"Xiaochun Cao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Xiaochun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135912106","display_name":"Zhifeng Hao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hao, Zhifeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135970207","display_name":"Xun Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xun","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":0.21699999272823334,"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":0.21699999272823334,"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":0.19760000705718994,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.10329999774694443,"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/inversion","display_name":"Inversion (geology)","score":0.5715000033378601},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4977000057697296},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4740000069141388},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3400999903678894},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.28679999709129333},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.28380000591278076}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8420000076293945},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.5715000033378601},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4977000057697296},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4740000069141388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42080000042915344},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3400999903678894},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2874999940395355},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2671000063419342},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2597000002861023},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.25600001215934753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.10748","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10748","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.2605.10748","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.10748","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"One-Shot":[0],"Federated":[1,51],"Learning,":[2],"where":[3],"a":[4,8,12,19,49,103,156],"central":[5],"server":[6],"learns":[7],"global":[9,62],"model":[10,63,76],"in":[11],"single":[13],"communication":[14],"round,":[15],"has":[16],"emerged":[17],"as":[18],"promising":[20],"paradigm.":[21],"However,":[22],"under":[23,171],"extremely":[24],"non-IID":[25],"settings,":[26],"existing":[27,169],"data-free":[28],"methods":[29],"often":[30],"generate":[31],"low-quality":[32],"data":[33,79],"that":[34,97,136,165],"suffers":[35],"from":[36,144],"severe":[37],"semantic":[38,83],"misalignment":[39],"with":[40,107,116],"ground-truth":[41],"labels.":[42],"To":[43,92],"overcome":[44],"these":[45],"issues,":[46],"we":[47,101],"propose":[48],"novel":[50],"Model":[52,138],"Inversion":[53,139],"and":[54],"Token":[55,148],"Relabel":[56,149],"(FedMITR)":[57],"framework,":[58],"which":[59],"trains":[60],"the":[61,87],"by":[64],"fully":[65],"exploiting":[66],"all":[67],"patches":[68,106,115],"of":[69,89],"synthetic":[70],"images.":[71],"Specifically,":[72],"FedMITR":[73,166],"employs":[74],"sparse":[75],"inversion":[77,88],"during":[78],"generation,":[80],"selectively":[81],"inverting":[82],"foregrounds":[84],"while":[85,114,147],"halting":[86],"uninformative":[90],"backgrounds.":[91],"address":[93],"semantically":[94],"meaningless":[95],"tokens":[96],"hinder":[98],"ViT":[99],"predictions,":[100],"implement":[102],"differentiated":[104],"strategy:":[105],"high":[108],"information":[109,118],"density":[110,119],"utilize":[111],"generated":[112],"pseudo-labels,":[113],"low":[117],"are":[120],"relabeled":[121],"via":[122],"ensemble":[123],"models":[124],"for":[125],"robust":[126],"distillation.":[127],"Theoretically,":[128],"our":[129],"analysis":[130],"based":[131],"on":[132],"algorithmic":[133],"stability":[134],"reveals":[135],"Sparse":[137],"eliminates":[140],"gradient":[141,152],"instability":[142],"arising":[143],"background":[145],"noise,":[146],"effectively":[150],"reduces":[151],"variance,":[153],"collectively":[154],"guaranteeing":[155],"tighter":[157],"generalization":[158],"bound.":[159],"Empirically,":[160],"extensive":[161],"experimental":[162],"results":[163],"demonstrate":[164],"substantially":[167],"outperforms":[168],"baselines":[170],"various":[172],"settings.":[173]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-13T00:00:00"}