{"id":"https://openalex.org/W7154341165","doi":"https://doi.org/10.1007/s42484-026-00385-6","title":"Q-VFL: quantum-enhanced vertical federated learning with contrastive encoding for privacy-preserving medical AI","display_name":"Q-VFL: quantum-enhanced vertical federated learning with contrastive encoding for privacy-preserving medical AI","publication_year":2026,"publication_date":"2026-04-14","ids":{"openalex":"https://openalex.org/W7154341165","doi":"https://doi.org/10.1007/s42484-026-00385-6"},"language":"en","primary_location":{"id":"doi:10.1007/s42484-026-00385-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42484-026-00385-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42484-026-00385-6.pdf","source":{"id":"https://openalex.org/S4210217596","display_name":"Quantum Machine Intelligence","issn_l":"2524-4906","issn":["2524-4906","2524-4914"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quantum Machine Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s42484-026-00385-6.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103086719","display_name":"Asitha Kottahachchi Kankanamge Don","orcid":"https://orcid.org/0009-0001-7637-4928"},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Asitha Kottahachchi Kankanamge Don","raw_affiliation_strings":["School of Computing Technologies, RMIT University, P.O. Box 3000, 3000, City, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Technologies, RMIT University, P.O. Box 3000, 3000, City, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5133617277","display_name":"Ibrahim Khalil","orcid":null},"institutions":[{"id":"https://openalex.org/I82951845","display_name":"RMIT University","ror":"https://ror.org/04ttjf776","country_code":"AU","type":"education","lineage":["https://openalex.org/I82951845"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ibrahim Khalil","raw_affiliation_strings":["School of Computing Technologies, RMIT University, P.O. Box 3000, 3000, City, Victoria, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing Technologies, RMIT University, P.O. Box 3000, 3000, City, Victoria, Australia","institution_ids":["https://openalex.org/I82951845"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103086719"],"corresponding_institution_ids":["https://openalex.org/I82951845"],"apc_list":{"value":2990,"currency":"USD","value_usd":2990},"apc_paid":{"value":2990,"currency":"USD","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41575098,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":"1","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":0.6241000294685364,"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":0.6241000294685364,"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/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.05169999971985817,"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":0.03849999979138374,"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/federated-learning","display_name":"Federated learning","score":0.6506999731063843},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5846999883651733},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5259000062942505},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4936999976634979},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.484499990940094},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4745999872684479},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4717000126838684},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.46129998564720154},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4586000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8009999990463257},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6506999731063843},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5846999883651733},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5421000123023987},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5259000062942505},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4936999976634979},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.484499990940094},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4745999872684479},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4717000126838684},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.46129998564720154},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4586000144481659},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43479999899864197},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.40130001306533813},{"id":"https://openalex.org/C1893757","wikidata":"https://www.wikidata.org/wiki/Q3653001","display_name":"Inversion (geology)","level":3,"score":0.3659999966621399},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.3352999985218048},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.3206999897956848},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3190999925136566},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.31279999017715454},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3093000054359436},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3037000000476837},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.29280000925064087},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s42484-026-00385-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42484-026-00385-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42484-026-00385-6.pdf","source":{"id":"https://openalex.org/S4210217596","display_name":"Quantum Machine Intelligence","issn_l":"2524-4906","issn":["2524-4906","2524-4914"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quantum Machine Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s42484-026-00385-6","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s42484-026-00385-6","pdf_url":"https://link.springer.com/content/pdf/10.1007/s42484-026-00385-6.pdf","source":{"id":"https://openalex.org/S4210217596","display_name":"Quantum Machine Intelligence","issn_l":"2524-4906","issn":["2524-4906","2524-4914"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Quantum Machine Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.49103882908821106,"id":"https://metadata.un.org/sdg/1","display_name":"No poverty"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320310598","display_name":"Amazon Web Services","ror":"https://ror.org/04mv4n011"},{"id":"https://openalex.org/F4320320972","display_name":"RMIT University","ror":"https://ror.org/04ttjf776"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7154341165.pdf","grobid_xml":"https://content.openalex.org/works/W7154341165.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W1762828224","https://openalex.org/W2051267297","https://openalex.org/W2535690855","https://openalex.org/W2559394418","https://openalex.org/W2781738013","https://openalex.org/W2792946961","https://openalex.org/W2963209930","https://openalex.org/W2982169647","https://openalex.org/W3012501605","https://openalex.org/W3045674654","https://openalex.org/W3075559820","https://openalex.org/W3156286751","https://openalex.org/W3182158470","https://openalex.org/W3200759624","https://openalex.org/W4205164650","https://openalex.org/W4213212652","https://openalex.org/W4214758645","https://openalex.org/W4224441431","https://openalex.org/W4224879845","https://openalex.org/W4312298950","https://openalex.org/W4317436377","https://openalex.org/W4366389470","https://openalex.org/W4372055232","https://openalex.org/W4391250546","https://openalex.org/W4395001524","https://openalex.org/W4399158318","https://openalex.org/W4401942562","https://openalex.org/W4411768502","https://openalex.org/W4412030706","https://openalex.org/W4412985346"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Medical":[1],"institutions":[2],"face":[3],"significant":[4],"challenges":[5],"in":[6,38],"collaborative":[7,185],"machine":[8],"learning":[9,27,85],"due":[10],"to":[11,108,153],"diverse":[12,90],"feature":[13,57,73],"distributions,":[14],"strict":[15],"privacy":[16,36,137],"regulations,":[17],"and":[18,34,116,131,184],"the":[19,96,125],"critical":[20],"issue":[21],"of":[22,106],"data":[23],"scarcity.":[24],"Traditional":[25],"federated":[26],"approaches":[28],"often":[29],"struggle":[30],"with":[31,59,103,158],"poor":[32],"generalization":[33],"inadequate":[35],"protection":[37],"these":[39],"sensitive":[40],"healthcare":[41],"settings.":[42],"This":[43],"paper":[44],"presents":[45],"a":[46,66,82,179],"Quantum-Enhanced":[47],"Vertical":[48],"Federated":[49],"Learning":[50],"(Q-VFL)":[51],"framework":[52,99,126],"that":[53,95,142],"effectively":[54],"combines":[55],"quantum":[56,68,113],"encoding":[58],"supervised":[60],"contrastive":[61],"learning.":[62],"Our":[63],"approach":[64],"employs":[65],"three-stage":[67],"circuit":[69],"architecture":[70],"for":[71,182],"robust":[72],"encoding,":[74],"followed":[75],"by":[76,150],"classical":[77,120,155],"neural":[78],"networks,":[79],"implemented":[80],"through":[81],"privacy-preserving":[83],"split":[84],"procedure.":[86],"Experimental":[87],"results":[88,118],"across":[89],"medical":[91,186],"imaging":[92],"datasets":[93,172],"demonstrate":[94],"proposed":[97],"Q-VFL":[98,143,163,177],"achieves":[100,165],"strong":[101],"performance,":[102,124],"accuracy":[104],"gains":[105],"up":[107],"25":[109],"percentage":[110],"points":[111],"over":[112],"cross-entropy":[114],"baselines":[115],"competitive":[117],"against":[119],"methods.":[121],"Beyond":[122],"raw":[123],"exhibits":[127],"high":[128],"clinical":[129],"reliability":[130],"stability.":[132],"Crucially,":[133],"it":[134],"provides":[135],"stronger":[136],"guarantees:":[138],"detailed":[139],"analysis":[140],"shows":[141],"reduces":[144],"membership":[145],"inference":[146],"attack":[147],"success":[148,169],"rates":[149],"10\u201315%":[151],"relative":[152],"deep":[154],"VFL":[156],"baselines,":[157],"avg.":[159],"0.59":[160],"vs.":[161],"0.68.":[162],"also":[164],"near-zero":[166],"model":[167],"inversion":[168],"on":[170],"MedMNIST":[171],"(0.001).":[173],"These":[174],"findings":[175],"identify":[176],"as":[178],"promising":[180],"solution":[181],"secure":[183],"AI.":[187]},"counts_by_year":[],"updated_date":"2026-08-06T08:24:18.245995","created_date":"2026-04-15T00:00:00"}