{"id":"https://openalex.org/W7161737599","doi":"https://doi.org/10.48550/arxiv.2605.18554","title":"Federated Martingale Posterior Samping","display_name":"Federated Martingale Posterior Samping","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161737599","doi":"https://doi.org/10.48550/arxiv.2605.18554"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18554","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.2605.18554","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136460503","display_name":"Boning Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Boning","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076020595","display_name":"Matteo Zecchin","orcid":"https://orcid.org/0000-0003-3458-2881"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zecchin, Matteo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109675095","display_name":"Mingzhao Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Mingzhao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038776407","display_name":"Dongzhu Liu","orcid":"https://orcid.org/0000-0001-7820-9531"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Dongzhu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136500578","display_name":"Osvaldo Simeone","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Simeone, Osvaldo","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":0.3028999865055084,"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":0.3028999865055084,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.11699999868869781,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06830000132322311,"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/martingale","display_name":"Martingale (probability theory)","score":0.5597000122070312},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.48660001158714294},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47999998927116394},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3846000134944916},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3400999903678894},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.3197999894618988},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3179999887943268},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.3116999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6137999892234802},{"id":"https://openalex.org/C48406656","wikidata":"https://www.wikidata.org/wiki/Q534112","display_name":"Martingale (probability theory)","level":2,"score":0.5597000122070312},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.48660001158714294},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4059000015258789},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38510000705718994},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.37299999594688416},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3598000109195709},{"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/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.3116999864578247},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3077000081539154},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3061000108718872},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2971999943256378},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C166785042","wikidata":"https://www.wikidata.org/wiki/Q190391","display_name":"Central limit theorem","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26750001311302185},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C103613024","wikidata":"https://www.wikidata.org/wiki/Q230924","display_name":"Stateless protocol","level":3,"score":0.2637999951839447},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2583000063896179},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.25099998712539673}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18554","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.2605.18554","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18554","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],"Bayesian":[1],"neural":[2],"networks":[3],"require":[4,89],"fixing":[5],"a":[6,14,67,105,115],"prior":[7],"on":[8,19,131],"the":[9,20,43,54,63,81,93,123,126,141],"model":[10],"parameters":[11],"together":[12],"with":[13,66],"likelihood.":[15],"Eliciting":[16],"meaningful":[17],"priors":[18],"weight":[21],"space":[22],"of":[23,32,46,118],"modern":[24],"overparameterized":[25],"models":[26,48],"is":[27],"notoriously":[28],"difficult,":[29],"and":[30,39,70,79,122,134,144],"misspecification":[31],"either":[33],"component":[34],"can":[35],"severely":[36],"degrade":[37],"accuracy":[38],"calibration.":[40],"Motivated":[41],"by":[42,74],"rapid":[44],"progress":[45],"predictive":[47,60,68,77,127],"such":[49],"as":[50,59],"large":[51],"language":[52],"models,":[53],"martingale":[55,101],"posterior,":[56],"also":[57],"known":[58],"Bayes,":[61],"replaces":[62],"prior--likelihood":[64],"pair":[65],"distribution":[69],"recovers":[71],"parameter":[72],"uncertainty":[73],"repeatedly":[75],"drawing":[76],"samples":[78],"refitting":[80],"model.":[82],"A":[83],"direct":[84],"federated":[85],"implementation,":[86],"however,":[87],"would":[88],"clients":[90],"to":[91],"share":[92],"local":[94],"data":[95,120],"sets.":[96],"This":[97],"letter":[98],"proposes":[99],"{federated":[100],"posterior}":[102],"(FMP)":[103],"sampling,":[104],"one-shot":[106],"embarrassingly":[107],"parallel":[108],"protocol":[109],"in":[110],"which":[111],"each":[112],"client":[113],"uploads":[114],"small":[116],"set":[117],"trainable":[119],"embeddings":[121],"server":[124],"runs":[125],"sampler":[128],"centrally.":[129],"Experiments":[130],"MNIST,":[132],"CIFAR-10,":[133],"CIFAR-100":[135],"show":[136],"that":[137],"FMP":[138],"closely":[139],"matches":[140],"centralized":[142],"counterpart":[143],"significantly":[145],"improves":[146],"calibration":[147],"over":[148],"consensus-style":[149],"baselines.":[150]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-20T00:00:00"}