{"id":"https://openalex.org/W7160912064","doi":"https://doi.org/10.1145/3805689.3812242","title":"Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning","display_name":"Toward Individual Fairness Without Centralized Data: Selective Counterfactual Consistency for Vertical Federated Learning","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7160912064","doi":"https://doi.org/10.1145/3805689.3812242"},"language":null,"primary_location":{"id":"doi:10.1145/3805689.3812242","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3805689.3812242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2605.07117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059869945","display_name":"Dawood Wasif","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawood Wasif","raw_affiliation_strings":["Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-0513-5890","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101985412","display_name":"Chandan K. Reddy","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chandan K. Reddy","raw_affiliation_strings":["Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA"],"raw_orcid":"https://orcid.org/0000-0003-2839-3662","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135954328","display_name":"Terrence J. Moore","orcid":null},"institutions":[{"id":"https://openalex.org/I166416128","display_name":"DEVCOM Army Research Laboratory","ror":"https://ror.org/011hc8f90","country_code":"US","type":"government","lineage":["https://openalex.org/I1304082316","https://openalex.org/I1330347796","https://openalex.org/I166416128","https://openalex.org/I2802705668","https://openalex.org/I4210154437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Terrence J. Moore","raw_affiliation_strings":["U.S. Army Research Laboratory, Adelphi, MD, USA"],"raw_orcid":"https://orcid.org/0000-0003-3279-2965","affiliations":[{"raw_affiliation_string":"U.S. Army Research Laboratory, Adelphi, MD, USA","institution_ids":["https://openalex.org/I166416128"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135920016","display_name":"Jin-Hee Cho","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin-Hee Cho","raw_affiliation_strings":["Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA"],"raw_orcid":"https://orcid.org/0000-0002-5908-4662","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Virginia Polytechnic Institute and State University, Alexandria, VA, USA","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"921","last_page":"955"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8669999837875366,"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":0.8669999837875366,"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/T10883","display_name":"Ethics and Social Impacts of AI","score":0.045899998396635056,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.00989999994635582,"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/counterfactual-thinking","display_name":"Counterfactual thinking","score":0.8985000252723694},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6467000246047974},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.553600013256073},{"id":"https://openalex.org/keywords/enforcement","display_name":"Enforcement","score":0.51910001039505},{"id":"https://openalex.org/keywords/sketch","display_name":"Sketch","score":0.4189000129699707},{"id":"https://openalex.org/keywords/causal-consistency","display_name":"Causal consistency","score":0.3864000141620636},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.35989999771118164},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.35510000586509705},{"id":"https://openalex.org/keywords/parity","display_name":"Parity (physics)","score":0.35120001435279846},{"id":"https://openalex.org/keywords/weak-consistency","display_name":"Weak consistency","score":0.3361000120639801}],"concepts":[{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.8985000252723694},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6800000071525574},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6467000246047974},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.553600013256073},{"id":"https://openalex.org/C2779777834","wikidata":"https://www.wikidata.org/wiki/Q4202277","display_name":"Enforcement","level":2,"score":0.51910001039505},{"id":"https://openalex.org/C2779231336","wikidata":"https://www.wikidata.org/wiki/Q7534724","display_name":"Sketch","level":2,"score":0.4189000129699707},{"id":"https://openalex.org/C175652121","wikidata":"https://www.wikidata.org/wiki/Q4379351","display_name":"Causal consistency","level":5,"score":0.3864000141620636},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.36149999499320984},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.35989999771118164},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2777151079","wikidata":"https://www.wikidata.org/wiki/Q141160","display_name":"Parity (physics)","level":2,"score":0.35120001435279846},{"id":"https://openalex.org/C122377713","wikidata":"https://www.wikidata.org/wiki/Q4422799","display_name":"Weak consistency","level":4,"score":0.3361000120639801},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3301999866962433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3203999996185303},{"id":"https://openalex.org/C27415008","wikidata":"https://www.wikidata.org/wiki/Q7256382","display_name":"Psychological intervention","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C33762810","wikidata":"https://www.wikidata.org/wiki/Q461671","display_name":"Data integrity","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.2994000017642975},{"id":"https://openalex.org/C71889745","wikidata":"https://www.wikidata.org/wiki/Q1783264","display_name":"Counterfactual conditional","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C2776261394","wikidata":"https://www.wikidata.org/wiki/Q315562","display_name":"Impossibility","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C63428231","wikidata":"https://www.wikidata.org/wiki/Q9043","display_name":"Norwegian","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.26820001006126404},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C93361087","wikidata":"https://www.wikidata.org/wiki/Q4426698","display_name":"Data consistency","level":2,"score":0.26460000872612},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.2621000111103058},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2590000033378601},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3805689.3812242","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3805689.3812242","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2605.07117","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.07117","pdf_url":"https://arxiv.org/pdf/2605.07117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2605.07117","is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.07117","pdf_url":"https://arxiv.org/pdf/2605.07117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","score":0.8291041254997253,"id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G3052087145","display_name":"Collaborative Research: III: Small: Advancing Data-Centric AI through Generative Approaches for Feature Space Reconstruction","funder_award_id":"2416728","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G704340294","display_name":null,"funder_award_id":"W911NF-24-2-0241","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"},{"id":"https://openalex.org/G7452299184","display_name":null,"funder_award_id":"W911NF","funder_id":"https://openalex.org/F4320338281","funder_display_name":"Army Research Office"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160912064.pdf","grobid_xml":"https://content.openalex.org/works/W7160912064.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"When":[0],"algorithmic":[1],"decisions":[2],"depend":[3],"on":[4,22,64],"data":[5],"distributed":[6],"across":[7,39,54],"institutions,":[8],"how":[9],"can":[10,51],"we":[11],"ensure":[12],"that":[13,152,165,185],"an":[14,181],"individual's":[15],"outcome":[16],"does":[17,153],"not":[18,154],"change":[19],"arbitrarily":[20],"based":[21],"a":[23,97,114,139,148],"protected":[24,49],"attribute?":[25],"We":[26,94],"study":[27],"this":[28],"question":[29],"in":[30,77,110,236],"vertical":[31],"federated":[32],"learning":[33],"(VFL),":[34],"where":[35],"features":[36],"are":[37],"split":[38],"parties,":[40],"sensitive":[41,145],"attributes":[42],"may":[43],"be":[44,52],"private,":[45,124],"and":[46,134,172,176,200,229],"proxies":[47,136],"for":[48,100],"characteristics":[50],"scattered":[53],"institutional":[55],"boundaries":[56],"under":[57,72,190],"strict":[58],"privacy":[59,151],"constraints.":[60],"Our":[61],"focus":[62],"is":[63],"individual-level":[65],"counterfactual":[66,103,163],"stability,":[67],"i.e.,":[68],"per-instance":[69],"prediction":[70,188],"consistency":[71,104,183],"protected-attribute":[73,191],"interventions":[74],"as":[75,88],"formalized":[76],"the":[78,107,144,157],"causal":[79],"fairness":[80],"literature,":[81],"rather":[82],"than":[83],"group":[84],"parity":[85,90],"guarantees":[86],"such":[87],"demographic":[89],"or":[91,205],"equalized":[92],"odds.":[93],"propose":[95],"SCC-VFL,":[96],"server-centric":[98],"framework":[99],"enforcing":[101],"selective":[102],"(SCC)":[105],"at":[106],"individual":[108],"level":[109],"VFL.":[111],"SCC-VFL":[112,203],"operationalizes":[113],"given":[115],"policy":[116],"specification":[117],"by":[118,215],"combining":[119],"three":[120,194],"components:":[121],"(i)":[122],"differentially":[123],"graph-free":[125],"discovery":[126],"of":[127,143],"feature":[128],"roles":[129],"into":[130],"non-descendants,":[131],"policy-permitted":[132],"mediators,":[133],"impermissible":[135,187],"using":[137],"only":[138,167],"formally":[140],"private":[141],"sketch":[142],"attribute,":[146],"with":[147],"formal":[149],"per-release":[150],"extend":[155],"to":[156,217,220],"full":[158],"training":[159],"pipeline;":[160],"(ii)":[161],"masked":[162],"generation":[164],"edits":[166],"mediators":[168],"while":[169,209],"fixing":[170],"non-descendants":[171],"suppressing":[173],"proxy":[174],"leakage;":[175],"(iii)":[177],"server-side":[178],"enforcement":[179],"via":[180],"SCC":[182],"loss":[184],"penalizes":[186],"changes":[189],"interventions.":[192],"Across":[193],"real-world":[195],"datasets":[196],"spanning":[197],"credit,":[198],"healthcare,":[199],"criminal":[201],"justice,":[202],"maintains":[204],"improves":[206,230],"predictive":[207],"accuracy":[208],"sharply":[210],"reducing":[211],"decision":[212],"flip":[213],"rates":[214],"up":[216],"98%":[218],"relative":[219],"strong":[221],"baselines.":[222],"It":[223],"also":[224],"lowers":[225],"attribute-inference":[226],"attack":[227],"success":[228],"robustness,":[231],"demonstrating":[232],"favorable":[233],"utility-fairness-privacy":[234],"trade-offs":[235],"realistic":[237],"VFL":[238],"deployments.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}
