{"id":"https://openalex.org/W7128613821","doi":"https://doi.org/10.48550/arxiv.2602.09520","title":"Rashomon Sets and Model Multiplicity in Federated Learning","display_name":"Rashomon Sets and Model Multiplicity in Federated Learning","publication_year":2026,"publication_date":"2026-02-10","ids":{"openalex":"https://openalex.org/W7128613821","doi":"https://doi.org/10.48550/arxiv.2602.09520"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09520","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107018856","display_name":"Xenia Heilmann","orcid":"https://orcid.org/0000-0003-3758-9253"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Heilmann, Xenia","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093022526","display_name":"Luca Corbucci","orcid":"https://orcid.org/0000-0001-5427-5518"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Corbucci, Luca","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042265681","display_name":"Mattia Cerrato","orcid":"https://orcid.org/0000-0001-7736-0547"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cerrato, Mattia","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107018856"],"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":0.9190999865531921,"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":0.9190999865531921,"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/T11704","display_name":"Mobile Crowdsensing and Crowdsourcing","score":0.02710000053048134,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.003800000064074993,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5394999980926514},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.438400000333786},{"id":"https://openalex.org/keywords/set-operations","display_name":"Set operations","score":0.4269999861717224},{"id":"https://openalex.org/keywords/empirical-research","display_name":"Empirical research","score":0.38989999890327454},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.3481999933719635}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7174000144004822},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5394999980926514},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.45840001106262207},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C2777168461","wikidata":"https://www.wikidata.org/wiki/Q42196253","display_name":"Set operations","level":3,"score":0.4269999861717224},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2879999876022339},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2711000144481659}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09520","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09520","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09520","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09520","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"score":0.7530229687690735,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"Rashomon":[1,57,140,147,159,171,178,189,235],"set":[2,58,148,160,172,236],"captures":[3],"the":[4,22,53,111,136,146,174],"collection":[5],"of":[6,56,113,139,176,184],"models":[7,84,245],"that":[8,48,230,246],"achieve":[9],"near-identical":[10],"empirical":[11,220],"performance":[12],"yet":[13],"may":[14,118],"differ":[15],"substantially":[16],"in":[17,142],"their":[18,28,250],"decision":[19,45],"boundaries.":[20],"Understanding":[21],"differences":[23],"among":[24,153],"these":[25],"models,":[26],"i.e.,":[27],"multiplicity,":[29],"is":[30],"recognized":[31],"as":[32,42],"a":[33,86,114,157,169,181,213],"crucial":[34],"step":[35],"toward":[36],"model":[37,117],"transparency,":[38],"fairness,":[39],"and":[40,59,65,105,186,217,255],"robustness,":[41],"it":[43],"reveals":[44],"boundaries":[46],"instabilities":[47],"standard":[49,200,223],"metrics":[50,61,202],"obscure.":[51],"However,":[52],"existing":[54],"definitions":[55,237],"multiplicity":[60,201],"assume":[62],"centralized":[63],"learning":[64],"do":[66],"not":[67],"extend":[68],"naturally":[69],"to":[70,150,192,243],"decentralized,":[71],"multi-party":[72],"settings":[73],"like":[74],"Federated":[75],"Learning":[76],"(FL).":[77],"In":[78,108,131],"FL,":[79,151],"multiple":[80],"clients":[81,242],"collaboratively":[82],"train":[83],"under":[85,206],"central":[87],"server's":[88],"coordination":[89],"without":[90],"sharing":[91],"raw":[92],"data,":[93,252],"which":[94],"preserves":[95],"privacy":[96,208],"but":[97],"introduces":[98],"challenges":[99],"from":[100],"heterogeneous":[101],"client":[102],"data":[103],"distribution":[104],"communication":[106],"constraints.":[107,209],"this":[109,132],"setting,":[110],"choice":[112],"single":[115],"best":[116],"homogenize":[119],"predictive":[120],"behavior":[121],"across":[122,165,180],"diverse":[123],"clients,":[124,167,185],"amplify":[125],"biases,":[126],"or":[127],"undermine":[128],"fairness":[129,253],"guarantees.":[130],"work,":[133],"we":[134,144,197,211],"provide":[135],"first":[137],"formalization":[138],"sets":[141,179,190],"FL.First,":[143],"adapt":[145],"definition":[149],"distinguishing":[152],"three":[154,232],"perspectives:":[155],"(I)":[156],"global":[158],"defined":[161],"over":[162],"aggregated":[163],"statistics":[164],"all":[166,231],"(II)":[168],"t-agreement":[170],"representing":[173],"intersection":[175],"local":[177,195,251],"fraction":[182],"t":[183],"(III)":[187],"individual":[188],"specific":[191],"each":[193],"client's":[194],"distribution.Second,":[196],"show":[198],"how":[199],"can":[203],"be":[204],"estimated":[205],"FL's":[207],"Finally,":[210],"introduce":[212],"multiplicity-aware":[214],"FL":[215,224],"pipeline":[216],"conduct":[218],"an":[219],"study":[221],"on":[222],"benchmark":[225],"datasets.":[226],"Our":[227],"results":[228],"demonstrate":[229],"proposed":[233],"federated":[234],"offer":[238],"valuable":[239],"insights,":[240],"enabling":[241],"deploy":[244],"better":[247],"align":[248],"with":[249],"considerations,":[254],"practical":[256],"requirements.":[257]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-12T00:00:00"}
