{"id":"https://openalex.org/W4416251640","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227950","title":"Enhancing Secure Tree Training with Stacking Ensemble Method in Vertical Federated Learning on Non-IID Tabular Data","display_name":"Enhancing Secure Tree Training with Stacking Ensemble Method in Vertical Federated Learning on Non-IID Tabular Data","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251640","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227950"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5007492406","display_name":"Jiahui Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiahui Dai","raw_affiliation_strings":["Tongji University,School of Computer Science and Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100604556","display_name":"Congcong Chen","orcid":"https://orcid.org/0000-0002-1716-1332"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Congcong Chen","raw_affiliation_strings":["Tongji University,School of Computer Science and Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100497633","display_name":"Minyu Teng","orcid":"https://orcid.org/0009-0009-0896-5214"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minyu Teng","raw_affiliation_strings":["Tongji University,School of Computer Science and Technology,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Tongji University,School of Computer Science and Technology,Shanghai,China","institution_ids":["https://openalex.org/I116953780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5007492406"],"corresponding_institution_ids":["https://openalex.org/I116953780"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1948485,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9215999841690063,"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":0.9215999841690063,"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":0.03550000116229057,"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/T14347","display_name":"Big Data and Digital Economy","score":0.004000000189989805,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/encryption","display_name":"Encryption","score":0.70660001039505},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.685699999332428},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.4609000086784363},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4447000026702881},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.43230000138282776},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.4219000041484833},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.40849998593330383},{"id":"https://openalex.org/keywords/differential-privacy","display_name":"Differential privacy","score":0.39890000224113464},{"id":"https://openalex.org/keywords/differential","display_name":"Differential (mechanical device)","score":0.3732999861240387}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7915999889373779},{"id":"https://openalex.org/C148730421","wikidata":"https://www.wikidata.org/wiki/Q141090","display_name":"Encryption","level":2,"score":0.70660001039505},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.685699999332428},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6319000124931335},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.4609000086784363},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.4219000041484833},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.412200003862381},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.40849998593330383},{"id":"https://openalex.org/C23130292","wikidata":"https://www.wikidata.org/wiki/Q5275358","display_name":"Differential privacy","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39640000462532043},{"id":"https://openalex.org/C93226319","wikidata":"https://www.wikidata.org/wiki/Q193137","display_name":"Differential (mechanical device)","level":2,"score":0.3732999861240387},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3465999960899353},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3346000015735626},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.32829999923706055},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31850001215934753},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C10511746","wikidata":"https://www.wikidata.org/wiki/Q899388","display_name":"Data security","level":3,"score":0.3131999969482422},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C84839998","wikidata":"https://www.wikidata.org/wiki/Q5249245","display_name":"Decision rule","level":2,"score":0.303600013256073},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.3027999997138977},{"id":"https://openalex.org/C178489894","wikidata":"https://www.wikidata.org/wiki/Q8789","display_name":"Cryptography","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2797999978065491},{"id":"https://openalex.org/C5481197","wikidata":"https://www.wikidata.org/wiki/Q16766476","display_name":"Decision tree learning","level":3,"score":0.27309998869895935},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W2068714596","https://openalex.org/W2074388704","https://openalex.org/W2092071682","https://openalex.org/W2109426455","https://openalex.org/W2154496743","https://openalex.org/W2159915142","https://openalex.org/W2295598076","https://openalex.org/W2782492233","https://openalex.org/W2890376446","https://openalex.org/W2951761234","https://openalex.org/W2998207486","https://openalex.org/W3162518478","https://openalex.org/W3164712068","https://openalex.org/W4232478844","https://openalex.org/W4285603347","https://openalex.org/W4287332481","https://openalex.org/W4289824680","https://openalex.org/W4302734258","https://openalex.org/W4308643126","https://openalex.org/W4312646383","https://openalex.org/W4312944124","https://openalex.org/W4376851503","https://openalex.org/W4383501767","https://openalex.org/W4387105517","https://openalex.org/W4387717607","https://openalex.org/W4392903986","https://openalex.org/W4392904009","https://openalex.org/W4396215566","https://openalex.org/W4401070854","https://openalex.org/W4403908451"],"related_works":[],"abstract_inverted_index":{"Tabular":[0],"data":[1,22,53],"are":[2,96,173],"widely":[3],"used":[4],"in":[5,83,98,161],"practice,":[6],"and":[7,39,76,93,132,181,204,226,232,236,245,250],"decision":[8,18,33,162],"trees":[9,19,34,163],"can":[10],"effectively":[11],"process":[12],"such":[13],"data.":[14],"However,":[15],"training":[16,92,121,140,152],"high-quality":[17],"with":[20,124,197,209,216],"tabular":[21,52],"from":[23,51],"a":[24,72,118,150],"single":[25],"entity":[26],"is":[27,71,109],"often":[28],"insufficient.":[29],"Therefore,":[30],"vertical":[31],"federated":[32,85],"based":[35],"on":[36,88,165,230],"order-preserving":[37,195],"encryption":[38,107,126,190,196],"differential":[40,198,205],"privacy":[41],"(OP-VFDT)":[42],"have":[43],"been":[44],"proposed":[45,220],"as":[46],"effective":[47],"methods":[48],"for":[49,186,248],"learning":[50,86],"distributed":[54,78],"across":[55],"multiple":[56],"entities.":[57],"Nevertheless,":[58],"OP-VFDT\u2019s":[59],"prediction":[60,130],"performance":[61,131],"may":[62],"degrade":[63],"due":[64],"to":[65,111,128,175,224,242],"partial":[66,135],"overlapping":[67,136,169,211],"attribute":[68,137,170,212],"skew,":[69],"which":[70,95],"type":[73],"of":[74,145],"nonindependent":[75],"identically":[77],"(Non-IID)":[79],"divergences.":[80],"Existing":[81],"solutions":[82],"common":[84],"focus":[87],"opti-mizing":[89],"local":[90],"model":[91],"updates,":[94],"absent":[97],"OP-VFDT,":[99],"making":[100],"them":[101],"inapplicable.":[102],"Additionally,":[103],"OP-VFDT":[104,120],"using":[105],"deterministic":[106],"algorithms":[108],"vulnerable":[110],"frequency-analyzing":[112,203],"attacks.In":[113],"this":[114],"paper,":[115],"we":[116],"propose":[117],"novel":[119],"method":[122,141,221],"along":[123],"an":[125],"algorithm":[127],"enhance":[129],"security":[133],"under":[134],"skew.":[138],"The":[139,189],"employs":[142],"the":[143,155,177,183,217,219,238],"idea":[144],"stacking":[146],"ensemble":[147,187],"learning,":[148],"implementing":[149],"two-layer":[151],"architecture.":[153],"Leveraging":[154],"characteristic":[156],"that":[157],"each":[158],"node":[159],"splitting":[160],"relies":[164],"one":[166],"attribute,":[167],"two":[168],"restructuring":[171],"strategies":[172],"devised":[174],"train":[176],"first-layer":[178],"XGBoost":[179],"models":[180],"enrich":[182],"feature":[184],"information":[185],"learning.":[188],"algorithm,":[191],"by":[192,240],"synthesizing":[193],"non-deterministic":[194],"privacy,":[199],"enables":[200],"resistance":[201],"against":[202],"attacks.":[206],"In":[207],"experiments":[208],"varying":[210],"noise":[213],"scales,":[214],"compared":[215],"baseline,":[218],"achieved":[222],"up":[223,241],"5.55%":[225],"4.79%":[227],"accuracy":[228],"improvements":[229],"Covtype":[231],"Poker":[233],"datasets,":[234,252],"respectively,":[235],"reduced":[237],"MSE":[239],"3.29":[243],"(19.12%)":[244],"68.46":[246],"(86.80%)":[247],"CASP":[249],"CCPP":[251],"respectively.":[253]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}
