{"id":"https://openalex.org/W3208814888","doi":"https://doi.org/10.1145/3474369.3486872","title":"FedV","display_name":"FedV","publication_year":2021,"publication_date":"2021-10-28","ids":{"openalex":"https://openalex.org/W3208814888","doi":"https://doi.org/10.1145/3474369.3486872","mag":"3208814888"},"language":"en","primary_location":{"id":"doi:10.1145/3474369.3486872","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474369.3486872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101785871","display_name":"Runhua Xu","orcid":"https://orcid.org/0000-0003-4541-9764"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Runhua Xu","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010677582","display_name":"Nathalie Baracaldo","orcid":"https://orcid.org/0000-0001-9469-045X"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nathalie Baracaldo","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101784389","display_name":"Yi Zhou","orcid":"https://orcid.org/0000-0003-3932-6422"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Zhou","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054645319","display_name":"Ali Anwar","orcid":"https://orcid.org/0000-0003-4487-2436"},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ali Anwar","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042767930","display_name":"James Joshi","orcid":"https://orcid.org/0000-0003-4519-9802"},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James Joshi","raw_affiliation_strings":["University of Pittsburgh, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"University of Pittsburgh, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5048671912","display_name":"Heiko Ludwig","orcid":null},"institutions":[{"id":"https://openalex.org/I1341412227","display_name":"IBM (United States)","ror":"https://ror.org/05hh8d621","country_code":"US","type":"company","lineage":["https://openalex.org/I1341412227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Heiko Ludwig","raw_affiliation_strings":["IBM Research, San Jose, CA, USA"],"affiliations":[{"raw_affiliation_string":"IBM Research, San Jose, CA, USA","institution_ids":["https://openalex.org/I1341412227"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5101785871"],"corresponding_institution_ids":["https://openalex.org/I1341412227"],"apc_list":null,"apc_paid":null,"fwci":6.7197,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97310882,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"181","last_page":"192"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T10237","display_name":"Cryptography and Data Security","score":0.9955000281333923,"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/T11612","display_name":"Stochastic Gradient Optimization Techniques","score":0.9781000018119812,"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/computer-science","display_name":"Computer science","score":0.8149117827415466},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.6133916974067688},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5794417858123779},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.543952465057373},{"id":"https://openalex.org/keywords/encryption","display_name":"Encryption","score":0.5342214703559875},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4937220513820648},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4759390354156494},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.41983771324157715},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.41539737582206726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3858550190925598},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3846035599708557},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.36449167132377625},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3513084650039673},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.24529078602790833},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.20063278079032898},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08889308571815491}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8149117827415466},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.6133916974067688},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5794417858123779},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.543952465057373},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.5342214703559875},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4937220513820648},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4759390354156494},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.41983771324157715},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.41539737582206726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3858550190925598},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3846035599708557},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.36449167132377625},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3513084650039673},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.24529078602790833},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.20063278079032898},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08889308571815491},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474369.3486872","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474369.3486872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W209244779","https://openalex.org/W1544327602","https://openalex.org/W1547612978","https://openalex.org/W1724472458","https://openalex.org/W2004957971","https://openalex.org/W2006664424","https://openalex.org/W2048994663","https://openalex.org/W2051267297","https://openalex.org/W2112340198","https://openalex.org/W2119948977","https://openalex.org/W2128309988","https://openalex.org/W2137596716","https://openalex.org/W2145029920","https://openalex.org/W2402235285","https://openalex.org/W2477068038","https://openalex.org/W2535199873","https://openalex.org/W2535690855","https://openalex.org/W2766917641","https://openalex.org/W2767024440","https://openalex.org/W2767079719","https://openalex.org/W2787005258","https://openalex.org/W2794685135","https://openalex.org/W2794753592","https://openalex.org/W2884943453","https://openalex.org/W2901617670","https://openalex.org/W2903389359","https://openalex.org/W2918900813","https://openalex.org/W2950462542","https://openalex.org/W2966577458","https://openalex.org/W2970606380","https://openalex.org/W2984242138","https://openalex.org/W3029558105","https://openalex.org/W3080261024","https://openalex.org/W3120740533","https://openalex.org/W3190474835","https://openalex.org/W4254213077","https://openalex.org/W4301884211"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W4287823391","https://openalex.org/W3013363440","https://openalex.org/W4312762663","https://openalex.org/W4317941881","https://openalex.org/W2046435967","https://openalex.org/W2100090372","https://openalex.org/W4361205702","https://openalex.org/W3080832531"],"abstract_inverted_index":{"Federated":[0],"learning":[1,12],"(FL)":[2],"has":[3],"been":[4],"proposed":[5],"to":[6,18,69,103,110,193,198],"allow":[7],"collaborative":[8],"training":[9,105,189],"of":[10,81,171,186,188],"machine":[11],"(ML)":[13],"models":[14,113,139,181],"among":[15,100,157],"multiple":[16,97,179],"parties":[17,60,158],"keep":[19],"their":[20],"data":[21,196],"private":[22],"and":[23,63,83,107,114,146,164,168,182,191],"only":[24,53,67],"model":[25],"updates":[26],"are":[27,61,66,85,108],"shared.":[28],"Most":[29],"existing":[30],"approaches":[31,92],"have":[32],"focused":[33],"on":[34],"horizontal":[35],"FL,":[36],"while":[37],"many":[38],"real":[39],"scenarios":[40],"follow":[41],"a":[42,47,70,125,184],"vertically-partitioned":[43],"FL":[44,75,95],"setup,":[45],"where":[46],"complete":[48,79],"feature":[49],"set":[50],"is":[51,76],"formed":[52],"when":[54],"all":[55],"the":[56,59,64,152,176,199],"datasets":[57],"from":[58],"combined,":[62],"labels":[65,82],"available":[68],"single":[71],"party.":[72],"Privacy-preserving":[73],"vertical":[74,94,132],"challenging":[77],"because":[78],"sets":[80,170],"features":[84],"not":[86],"owned":[87],"by":[88,159],"one":[89],"entity.":[90],"Existing":[91],"for":[93,127,134,154,166,178],"require":[96],"peer-to-peer":[98,155],"communications":[99],"parties,":[101],"leading":[102],"lengthy":[104],"times,":[106],"restricted":[109],"(approximated)":[111],"linear":[112,142],"just":[115],"two":[116],"parties.":[117,172],"To":[118],"close":[119],"this":[120],"gap,":[121],"we":[122],"propose":[123],"FedV,":[124],"framework":[126],"secure":[128],"gradient":[129],"computation":[130],"in":[131,195],"settings":[133],"several":[135],"widely":[136],"used":[137],"ML":[138,180],"such":[140],"as":[141],"models,":[143],"logistic":[144],"regression,":[145],"support":[147],"vector":[148],"machines.":[149],"FedV":[150],"removes":[151],"need":[153],"communication":[156],"using":[160],"functional":[161],"encryption":[162],"schemes":[163],"works":[165],"larger":[167],"changing":[169],"We":[173],"empirically":[174],"demonstrate":[175],"applicability":[177],"show":[183],"reduction":[185],"10%-70%":[187],"time":[190],"80%":[192],"90%":[194],"transfer":[197],"comparable":[200],"state-of-the-art":[201],"approaches.":[202]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":22},{"year":2024,"cited_by_count":19},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":13},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2021-11-08T00:00:00"}