{"id":"https://openalex.org/W4200406332","doi":"https://doi.org/10.1109/tpds.2021.3139191","title":"Mixing Activations and Labels in Distributed Training for Split Learning","display_name":"Mixing Activations and Labels in Distributed Training for Split Learning","publication_year":2021,"publication_date":"2021-12-29","ids":{"openalex":"https://openalex.org/W4200406332","doi":"https://doi.org/10.1109/tpds.2021.3139191"},"language":"en","primary_location":{"id":"doi:10.1109/tpds.2021.3139191","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2021.3139191","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","raw_type":"journal-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/A5041031990","display_name":"Danyang Xiao","orcid":"https://orcid.org/0000-0001-6798-9683"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Danyang Xiao","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102488059","display_name":"Chengang Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengang Yang","raw_affiliation_strings":["Guangdong Key Laboratory of Big Data Analysis and Processing, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Guangdong Key Laboratory of Big Data Analysis and Processing, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084826798","display_name":"Weigang Wu","orcid":"https://orcid.org/0000-0002-4714-7021"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weigang Wu","raw_affiliation_strings":["Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5041031990"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":1.5399,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.86353077,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9994000196456909,"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":0.9994000196456909,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9987999796867371,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9987000226974487,"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":0.8697701692581177},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.7426235675811768},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5414052605628967},{"id":"https://openalex.org/keywords/bipartite-graph","display_name":"Bipartite graph","score":0.539943277835846},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.49893712997436523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4643199145793915},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.45232659578323364},{"id":"https://openalex.org/keywords/data-sharing","display_name":"Data sharing","score":0.4265163540840149},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.4171363115310669},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3967716693878174},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.36394375562667847},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33594992756843567},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.27308323979377747},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20961597561836243}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8697701692581177},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.7426235675811768},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5414052605628967},{"id":"https://openalex.org/C197657726","wikidata":"https://www.wikidata.org/wiki/Q174733","display_name":"Bipartite graph","level":3,"score":0.539943277835846},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.49893712997436523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4643199145793915},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.45232659578323364},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.4265163540840149},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.4171363115310669},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3967716693878174},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.36394375562667847},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33594992756843567},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.27308323979377747},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20961597561836243},{"id":"https://openalex.org/C204787440","wikidata":"https://www.wikidata.org/wiki/Q188504","display_name":"Alternative medicine","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tpds.2021.3139191","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tpds.2021.3139191","pdf_url":null,"source":{"id":"https://openalex.org/S97130795","display_name":"IEEE Transactions on Parallel and Distributed Systems","issn_l":"1045-9219","issn":["1045-9219","1558-2183","2161-9883"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Parallel and Distributed Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.5199999809265137}],"awards":[{"id":"https://openalex.org/G4905520761","display_name":null,"funder_award_id":"U1801266","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6546932133","display_name":null,"funder_award_id":"U1711263","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1969365015","https://openalex.org/W2060393849","https://openalex.org/W2132737349","https://openalex.org/W2194775991","https://openalex.org/W2741815235","https://openalex.org/W2799040448","https://openalex.org/W2963209930","https://openalex.org/W2969388332","https://openalex.org/W2970408908","https://openalex.org/W2970941190","https://openalex.org/W2985580374","https://openalex.org/W2989060401","https://openalex.org/W2991040477","https://openalex.org/W3002824621","https://openalex.org/W3027472889","https://openalex.org/W3042621011","https://openalex.org/W3043303805","https://openalex.org/W3091884625","https://openalex.org/W3103107468","https://openalex.org/W3105915864","https://openalex.org/W3108109508","https://openalex.org/W3109503640","https://openalex.org/W3110176593","https://openalex.org/W3129519326","https://openalex.org/W3133202919","https://openalex.org/W3176364684","https://openalex.org/W4242989628","https://openalex.org/W4288075846","https://openalex.org/W4289147263","https://openalex.org/W4300833946","https://openalex.org/W4318619660","https://openalex.org/W6637373629","https://openalex.org/W6679393576","https://openalex.org/W6728757088","https://openalex.org/W6742075157","https://openalex.org/W6756436328","https://openalex.org/W6756718674","https://openalex.org/W6760184523","https://openalex.org/W6764733053","https://openalex.org/W6764838729","https://openalex.org/W6773112480","https://openalex.org/W6780438843"],"related_works":["https://openalex.org/W4323056121","https://openalex.org/W4246751904","https://openalex.org/W3215235441","https://openalex.org/W4323546569","https://openalex.org/W2894747149","https://openalex.org/W4306175410","https://openalex.org/W2996935211","https://openalex.org/W3129604848","https://openalex.org/W2339272062","https://openalex.org/W4200406332"],"abstract_inverted_index":{"Split":[0],"Learning":[1],"(SL)":[2],"is":[3],"a":[4,77,102,158,172],"distributed":[5],"machine":[6],"learning":[7],"setting":[8],"that":[9,100],"allows":[10],"several":[11],"nodes":[12],"to":[13,71,112,134],"train":[14],"neural":[15],"networks":[16],"based":[17,161],"on":[18],"model":[19,235],"parallelism.":[20],"Since":[21,141],"SL":[22,63],"avoids":[23],"sharing":[24,61],"raw":[25,42,58,108,130,221],"data":[26,33,43,54,109,131,222],"among":[27],"training":[28],"nodes,":[29],"it":[30],"can":[31,193,216],"protect":[32,120],"privacy":[34,55,67],"by":[35],"nature.":[36],"However,":[37],"recent":[38],"studies":[39],"show":[40,210],"that,":[41,211],"may":[44,52,64,147],"be":[45],"reconstructed":[46],"from":[47,137],"activations":[48,82,99,152,179,198,205],"in":[49,62],"training,":[50],"which":[51,168],"cause":[53,66],"leakage.":[56],"Besides":[57],"data,":[59],"label":[60,121,224],"also":[65],"problems.":[68],"In":[69],"order":[70],"address":[72],"these":[73],"issues,":[74],"we":[75,156],"propose":[76,157],"novel":[78],"mechanism":[79],"called":[80],"multiple":[81],"and":[83,153,180,199,206,223],"labels":[84,126,182,201],"mix":[85,194],"(MALM).":[86],"By":[87],"taking":[88],"advantage":[89],"of":[90,93,116,175,220],"the":[91,107,114,129,195,218],"diversity":[92],"sample":[94,145],"categories,":[95],"MALM":[96,123,215],"generates":[97],"mixed":[98,151,178,197],"preserve":[101],"low":[103],"distance":[104],"correlation":[105],"with":[106,128,143,171,185,190,202,213,227],"so":[110,132],"as":[111,133],"reduce":[113,217],"risk":[115,219],"reconstruction":[117],"attacks.":[118],"To":[119],"information,":[122],"creates":[124],"obfuscated":[125,154,181,200],"associated":[127],"prevent":[135],"adversaries":[136],"inferring":[138],"ground-truth":[139],"labels.":[140,207],"clients":[142,170,184,189],"few":[144,186,191],"categories":[146,176,192],"not":[148],"effectively":[149],"generate":[150],"labels,":[155],"bipartite":[159],"graph":[160],"assistant":[162],"client":[163],"match":[164],"technique":[165],"for":[166,183],"MALM,":[167],"lets":[169],"large":[173],"number":[174],"provide":[177],"categories.":[187],"Those":[188],"obtained":[196],"their":[203],"own":[204],"Experimental":[208],"results":[209],"compared":[212],"baselines,":[214],"information":[225],"leakage":[226],"lower":[228],"cost,":[229],"while":[230],"achieving":[231],"comparable":[232],"even":[233],"better":[234],"performance.":[236]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":3}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
