{"id":"https://openalex.org/W4307928442","doi":"https://doi.org/10.48550/arxiv.2210.16524","title":"Federated clustering with GAN-based data synthesis","display_name":"Federated clustering with GAN-based data synthesis","publication_year":2022,"publication_date":"2022-10-29","ids":{"openalex":"https://openalex.org/W4307928442","doi":"https://doi.org/10.48550/arxiv.2210.16524"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2210.16524","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.16524","pdf_url":"https://arxiv.org/pdf/2210.16524","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.16524","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100716664","display_name":"Jie Yan","orcid":"https://orcid.org/0000-0003-2054-6986"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Yan, Jie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100375136","display_name":"Jing Liu","orcid":"https://orcid.org/0000-0003-4690-1886"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Jing","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100612749","display_name":"Ji Qi","orcid":"https://orcid.org/0000-0003-4585-6534"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Ji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5108058566","display_name":"Zhongyuan Zhang","orcid":"https://orcid.org/0000-0003-3475-6271"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zhong-Yuan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100716664"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"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":0.970300018787384,"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":0.970300018787384,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9405999779701233,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"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/T12384","display_name":"Customer churn and segmentation","score":0.9301999807357788,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7726861238479614},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7465571165084839},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6172898411750793},{"id":"https://openalex.org/keywords/upload","display_name":"Upload","score":0.6159229278564453},{"id":"https://openalex.org/keywords/data-sharing","display_name":"Data sharing","score":0.5639190673828125},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5309422016143799},{"id":"https://openalex.org/keywords/fuzzy-clustering","display_name":"Fuzzy clustering","score":0.43643486499786377},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.16631683707237244},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.16515964269638062},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.09369441866874695}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7726861238479614},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7465571165084839},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6172898411750793},{"id":"https://openalex.org/C71901391","wikidata":"https://www.wikidata.org/wiki/Q7126699","display_name":"Upload","level":2,"score":0.6159229278564453},{"id":"https://openalex.org/C2779965156","wikidata":"https://www.wikidata.org/wiki/Q5227350","display_name":"Data sharing","level":3,"score":0.5639190673828125},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5309422016143799},{"id":"https://openalex.org/C17212007","wikidata":"https://www.wikidata.org/wiki/Q5511111","display_name":"Fuzzy clustering","level":3,"score":0.43643486499786377},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.16631683707237244},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.16515964269638062},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.09369441866874695},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C204787440","wikidata":"https://www.wikidata.org/wiki/Q188504","display_name":"Alternative medicine","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2210.16524","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.16524","pdf_url":"https://arxiv.org/pdf/2210.16524","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2210.16524","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2210.16524","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2210.16524","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.16524","pdf_url":"https://arxiv.org/pdf/2210.16524","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.49000000953674316},{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2945382830","https://openalex.org/W4224807364","https://openalex.org/W2596632494","https://openalex.org/W2535986621","https://openalex.org/W1980197432","https://openalex.org/W2382432689","https://openalex.org/W2000612978","https://openalex.org/W4388110928","https://openalex.org/W1483228865","https://openalex.org/W4312412183"],"abstract_inverted_index":{"Federated":[0],"clustering":[1,8,98,105],"(FC)":[2],"is":[3,15,62,129],"an":[4],"extension":[5],"of":[6,42,167],"centralized":[7,69],"in":[9,113,172],"federated":[10,97,104],"settings.":[11],"The":[12,135],"key":[13],"here":[14],"how":[16],"to":[17,34,51,59,63,83,122,143,150],"construct":[18],"a":[19,95],"global":[20,153],"similarity":[21,30,41,154],"measure":[22],"without":[23,158],"sharing":[24,159],"private":[25,160],"data,":[26],"since":[27],"the":[28,40,55,65,118,123,132,140,144,152,165,174,178],"local":[29,36],"may":[31],"be":[32,47],"insufficient":[33],"group":[35],"data":[37,87,102,121,137],"correctly":[38],"and":[39,75,116,147,177],"samples":[43],"across":[44],"clients":[45],"cannot":[46],"directly":[48],"measured":[49],"due":[50],"privacy":[52],"constraints.":[53],"Obviously,":[54],"most":[56],"straightforward":[57],"way":[58],"analyze":[60],"FC":[61],"employ":[64],"methods":[66],"extended":[67],"from":[68],"ones,":[70],"such":[71],"as":[72],"K-means":[73],"(KM)":[74],"fuzzy":[76],"c-means":[77],"(FCM).":[78],"However,":[79],"they":[80],"are":[81],"vulnerable":[82],"non":[84],"independent-and-identically-distributed":[85],"(non-IID)":[86],"among":[88],"clients.":[89],"To":[90],"handle":[91],"this,":[92],"we":[93],"propose":[94],"new":[96],"framework,":[99],"named":[100],"synthetic":[101,120,133,136],"aided":[103],"(SDA-FC).":[106],"It":[107],"trains":[108],"generative":[109],"adversarial":[110],"network":[111],"locally":[112],"each":[114],"client":[115],"uploads":[117],"generated":[119],"server,":[124],"where":[125],"KM":[126],"or":[127],"FCM":[128],"performed":[130],"on":[131],"data.":[134,161],"can":[138],"make":[139],"model":[141],"immune":[142],"non-IID":[145,175],"problem":[146,176],"enable":[148],"us":[149],"capture":[151],"characteristics":[155],"more":[156],"effectively":[157],"Comprehensive":[162],"experiments":[163],"reveals":[164],"advantages":[166],"SDA-FC,":[168],"including":[169],"superior":[170],"performance":[171],"addressing":[173],"device":[179],"failures.":[180]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}