{"id":"https://openalex.org/W4392903111","doi":"https://doi.org/10.1109/icassp48485.2024.10447282","title":"Federated CINN Clustering for Accurate Clustered Federated Learning","display_name":"Federated CINN Clustering for Accurate Clustered Federated Learning","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392903111","doi":"https://doi.org/10.1109/icassp48485.2024.10447282"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10447282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5085567926","display_name":"Yuhao Zhou","orcid":"https://orcid.org/0000-0002-3347-1357"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuhao Zhou","raw_affiliation_strings":["Sichuan University","Engineering Research Center of Machine Learning and Industry Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Engineering Research Center of Machine Learning and Industry Intelligence","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044196416","display_name":"Minjia Shi","orcid":"https://orcid.org/0000-0002-4990-6271"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minjia Shi","raw_affiliation_strings":["Sichuan University","Engineering Research Center of Machine Learning and Industry Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Engineering Research Center of Machine Learning and Industry Intelligence","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103250693","display_name":"Yuxin Tian","orcid":"https://orcid.org/0000-0003-3706-3462"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Tian","raw_affiliation_strings":["Sichuan University","Engineering Research Center of Machine Learning and Industry Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Engineering Research Center of Machine Learning and Industry Intelligence","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084090218","display_name":"Yuanxi Li","orcid":"https://orcid.org/0000-0003-0333-6039"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yuanxi Li","raw_affiliation_strings":["University of Illinois at Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Illinois at Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100347581","display_name":"Qing Ye","orcid":"https://orcid.org/0000-0003-3956-3348"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Ye","raw_affiliation_strings":["Sichuan University","Engineering Research Center of Machine Learning and Industry Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Engineering Research Center of Machine Learning and Industry Intelligence","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073535763","display_name":"Jiancheng Lv","orcid":"https://orcid.org/0000-0001-6551-3884"},"institutions":[{"id":"https://openalex.org/I24185976","display_name":"Sichuan University","ror":"https://ror.org/011ashp19","country_code":"CN","type":"education","lineage":["https://openalex.org/I24185976"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiancheng Lv","raw_affiliation_strings":["Sichuan University","Engineering Research Center of Machine Learning and Industry Intelligence"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sichuan University","institution_ids":["https://openalex.org/I24185976"]},{"raw_affiliation_string":"Engineering Research Center of Machine Learning and Industry Intelligence","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":5.5569,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.96299545,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5590","last_page":"5594"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9424999952316284,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9107000231742859,"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":0.8260443210601807},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.798201322555542},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7533106803894043},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5143759846687317},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.5011217594146729},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.47099560499191284},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47083258628845215},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4653205871582031},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4586044251918793}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8260443210601807},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.798201322555542},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7533106803894043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5143759846687317},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.5011217594146729},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.47099560499191284},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47083258628845215},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4653205871582031},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4586044251918793},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10447282","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10447282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.5400000214576721}],"awards":[{"id":"https://openalex.org/G522090842","display_name":null,"funder_award_id":"62306198","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":28,"referenced_works":["https://openalex.org/W1996881001","https://openalex.org/W2150593711","https://openalex.org/W2954340342","https://openalex.org/W2989289980","https://openalex.org/W2990789643","https://openalex.org/W2995022099","https://openalex.org/W3007345209","https://openalex.org/W3021654819","https://openalex.org/W3043723611","https://openalex.org/W3080934299","https://openalex.org/W3091635927","https://openalex.org/W3096831136","https://openalex.org/W3104631511","https://openalex.org/W3176364684","https://openalex.org/W3191604281","https://openalex.org/W4281753687","https://openalex.org/W4283796083","https://openalex.org/W4289147229","https://openalex.org/W4297798428","https://openalex.org/W4307823382","https://openalex.org/W4318619660","https://openalex.org/W6714644935","https://openalex.org/W6728757088","https://openalex.org/W6756756286","https://openalex.org/W6764817228","https://openalex.org/W6770590064","https://openalex.org/W6774120287","https://openalex.org/W6781318954"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W4378677776","https://openalex.org/W3013363440","https://openalex.org/W4287823391","https://openalex.org/W4390516098","https://openalex.org/W2181948922","https://openalex.org/W1975321310","https://openalex.org/W4312762663","https://openalex.org/W2384362569"],"abstract_inverted_index":{"Federated":[0,53],"Learning":[1],"(FL)":[2],"presents":[3],"an":[4],"innovative":[5],"approach":[6,164],"to":[7,40,58,88,104,128,168],"privacy-preserving":[8],"distributed":[9],"machine":[10],"learning":[11,151,177],"and":[12,73,116,133,157,172],"enables":[13],"efficient":[14],"crowd":[15,30],"intelligence":[16,31],"on":[17,154],"a":[18,22,85,101],"large":[19],"scale.":[20],"However,":[21],"significant":[23],"challenge":[24],"arises":[25],"when":[26],"coordinating":[27],"FL":[28],"with":[29,70],"which":[32,113],"diverse":[33],"client":[34],"groups":[35],"possess":[36],"disparate":[37],"objectives":[38],"due":[39],"data":[41,71,93],"heterogeneity":[42],"or":[43],"distinct":[44,138],"tasks.":[45,178],"To":[46],"address":[47],"this":[48],"challenge,":[49],"we":[50],"propose":[51],"the":[52,76,79,121,170],"cINN":[54],"Clustering":[55],"Algorithm":[56],"(FCCA)":[57],"robustly":[59],"cluster":[60],"clients":[61,69,132],"into":[62,94,137],"different":[63],"groups,":[64],"avoiding":[65],"mutual":[66],"interference":[67],"between":[68,131],"heterogeneity,":[72],"thereby":[74],"enhancing":[75],"performance":[77],"of":[78,174],"global":[80,86],"model.":[81],"Specifically,":[82],"FCCA":[83],"utilizes":[84],"encoder":[87],"transform":[89],"each":[90],"client\u2019s":[91],"private":[92],"multivariate":[95],"Gaussian":[96],"distributions.":[97],"It":[98],"then":[99],"employs":[100],"generative":[102],"model":[103],"learn":[105],"encoded":[106],"latent":[107],"features":[108],"through":[109],"maximum":[110],"likelihood":[111],"estimation,":[112],"eases":[114],"optimization":[115],"avoids":[117],"mode":[118],"collapse.":[119],"Finally,":[120],"central":[122],"server":[123],"collects":[124],"converged":[125],"local":[126],"models":[127,156],"approximate":[129],"similarities":[130],"thus":[134],"partition":[135],"them":[136],"clusters.":[139],"Extensive":[140],"experimental":[141],"results":[142,160],"demonstrate":[143],"FCCA\u2019s":[144],"superiority":[145],"over":[146],"other":[147],"state-of-the-art":[148],"clustered":[149],"federated":[150,176],"algorithms,":[152],"evaluated":[153],"various":[155],"datasets.":[158],"These":[159],"suggest":[161],"that":[162],"our":[163],"has":[165],"substantial":[166],"potential":[167],"enhance":[169],"efficiency":[171],"accuracy":[173],"real-world":[175]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11},{"year":2024,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
