{"id":"https://openalex.org/W7138273956","doi":"https://doi.org/10.48550/arxiv.2603.13617","title":"Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE","display_name":"Privacy-Preserving Federated Fraud Detection in Payment Transactions with NVIDIA FLARE","publication_year":2026,"publication_date":"2026-03-13","ids":{"openalex":"https://openalex.org/W7138273956","doi":"https://doi.org/10.48550/arxiv.2603.13617"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.13617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13617","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":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.2603.13617","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129718500","display_name":"Holger R. Roth","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Roth, Holger R.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129734416","display_name":"Sarthak Tickoo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tickoo, Sarthak","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129700670","display_name":"Mayank Kumar","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kumar, Mayank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054902298","display_name":"Isaac Yang","orcid":"https://orcid.org/0000-0002-5176-5615"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Isaac","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129650432","display_name":"Andrew Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129680376","display_name":"Amit Varshney","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varshney, Amit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129691802","display_name":"Sayani Kundu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kundu, Sayani","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005957806","display_name":"Iustina Vintila","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vintila, Iustina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129753045","display_name":"Peter Madsgaard","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madsgaard, Peter","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054422115","display_name":"Juraj Milcak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Milcak, Juraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006909076","display_name":"Chester Chen","orcid":"https://orcid.org/0009-0000-5835-6104"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chester","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129738230","display_name":"Yan Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066604966","display_name":"Andrew Feng","orcid":"https://orcid.org/0000-0002-1675-745X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Andrew","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129737074","display_name":"Jeff Savio","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savio, Jeff","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129659443","display_name":"Vikram Singh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Singh, Vikram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129668239","display_name":"Craig Stancill","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Stancill, Craig","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063904882","display_name":"Gloria W.Y. Wan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wan, Gloria","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129667328","display_name":"Evan Powell","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Powell, Evan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129687026","display_name":"Anwar Ul Haq","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haq, Anwar Ul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129729301","display_name":"Sudhir Upadhyay","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Upadhyay, Sudhir","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129700958","display_name":"Jisoo Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Jisoo","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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.38929998874664307,"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/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.38929998874664307,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.24140000343322754,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.059700001031160355,"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/interpretability","display_name":"Interpretability","score":0.781000018119812},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5558000206947327},{"id":"https://openalex.org/keywords/database-transaction","display_name":"Database transaction","score":0.4902999997138977},{"id":"https://openalex.org/keywords/payment","display_name":"Payment","score":0.4796000123023987},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4659999907016754},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.4643999934196472},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.45159998536109924},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.43230000138282776},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.4223000109195709},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4025999903678894}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.781000018119812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577999830245972},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C75949130","wikidata":"https://www.wikidata.org/wiki/Q848010","display_name":"Database transaction","level":2,"score":0.4902999997138977},{"id":"https://openalex.org/C145097563","wikidata":"https://www.wikidata.org/wiki/Q1148747","display_name":"Payment","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4659999907016754},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.4643999934196472},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.45159998536109924},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.4223000109195709},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3659000098705292},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.36550000309944153},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3411000072956085},{"id":"https://openalex.org/C164516710","wikidata":"https://www.wikidata.org/wiki/Q1166072","display_name":"Financial transaction","level":3,"score":0.33739998936653137},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3296000063419342},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2808000147342682},{"id":"https://openalex.org/C2779017730","wikidata":"https://www.wikidata.org/wiki/Q5145845","display_name":"Collaborative model","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.27790001034736633},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C72108876","wikidata":"https://www.wikidata.org/wiki/Q844565","display_name":"Transaction processing","level":3,"score":0.27300000190734863},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26969999074935913},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C127722929","wikidata":"https://www.wikidata.org/wiki/Q7833714","display_name":"Transaction data","level":3,"score":0.26750001311302185},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C47487241","wikidata":"https://www.wikidata.org/wiki/Q5227230","display_name":"Data access","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C513985346","wikidata":"https://www.wikidata.org/wiki/Q270471","display_name":"Virtualization","level":3,"score":0.2554999887943268},{"id":"https://openalex.org/C191087605","wikidata":"https://www.wikidata.org/wiki/Q1501395","display_name":"Online transaction processing","level":4,"score":0.2540999948978424},{"id":"https://openalex.org/C188087704","wikidata":"https://www.wikidata.org/wiki/Q369577","display_name":"Standardization","level":2,"score":0.2515999972820282},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.13617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13617","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.13617","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.13617","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.7242690324783325,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Fraud-related":[0],"financial":[1,49,84,163],"losses":[2],"continue":[3],"to":[4],"rise,":[5],"while":[6,132],"regulatory,":[7],"privacy,":[8],"and":[9,91,125,161,179,199],"data-sovereignty":[10],"constraints":[11],"increasingly":[12],"limit":[13],"the":[14,73,154],"feasibility":[15],"of":[16,82,116,157],"centralized":[17,128],"fraud":[18,89],"detection":[19,68],"systems.":[20],"Federated":[21],"Learning":[22],"(FL)":[23],"has":[24],"emerged":[25],"as":[26],"a":[27,60,79,98,113],"promising":[28],"paradigm":[29],"for":[30,69],"enabling":[31],"collaborative":[32],"model":[33,173],"training":[34,129],"across":[35],"institutions":[36],"without":[37],"sharing":[38],"raw":[39],"transaction":[40],"data.":[41],"Yet,":[42],"its":[43],"practical":[44],"effectiveness":[45],"under":[46,93],"realistic,":[47],"non-IID":[48],"data":[50,95,135],"distributions":[51],"remains":[52],"insufficiently":[53],"validated.":[54],"In":[55],"this":[56],"work,":[57],"we":[58,107,171,192],"present":[59],"multi-institution,":[61],"industry-oriented":[62],"proof-of-concept":[63],"study":[64],"evaluating":[65],"federated":[66,104,110,150,182],"anomaly":[67],"payment":[70],"transactions":[71],"using":[72,175],"NVIDIA":[74],"FLARE":[75],"framework.":[76],"We":[77,137],"simulate":[78],"realistic":[80],"federation":[81],"heterogeneous":[83],"institutions,":[85],"each":[86],"observing":[87],"distinct":[88],"typologies":[90],"operating":[92],"strict":[94],"isolation.":[96],"Using":[97],"deep":[99],"neural":[100],"network":[101],"trained":[102,122],"via":[103,197],"averaging":[105],"(FedAvg),":[106],"demonstrate":[108,200],"that":[109,143,181],"models":[111,123,183],"achieve":[112],"mean":[114],"F1-score":[115],"0.903":[117],"-":[118],"substantially":[119],"outperforming":[120],"locally":[121],"(0.643)":[124],"closely":[126],"approaching":[127],"performance":[130,145],"(0.925),":[131],"preserving":[133],"full":[134],"sovereignty.":[136],"further":[138],"analyze":[139],"convergence":[140],"behavior,":[141],"showing":[142],"strong":[144],"is":[146],"achieved":[147],"within":[148],"10":[149],"communication":[151],"rounds,":[152],"highlighting":[153],"operational":[155],"viability":[156],"FL":[158],"in":[159,168],"latency-":[160],"cost-sensitive":[162],"environments.":[164],"To":[165],"support":[166],"deployment":[167],"regulated":[169],"settings,":[170],"evaluate":[172],"interpretability":[174],"Shapley-based":[176],"feature":[177],"attribution":[178],"confirm":[180],"rely":[184],"on":[185],"semantically":[186],"coherent,":[187],"domain-relevant":[188],"decision":[189],"signals.":[190],"Finally,":[191],"incorporate":[193],"sample-level":[194],"differential":[195],"privacy":[196],"DP-SGD":[198],"favorable":[201],"privacy-utility":[202],"trade-offs...":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}
