{"id":"https://openalex.org/W4281753687","doi":"https://doi.org/10.1007/s11280-022-01046-x","title":"Multi-center federated learning: clients clustering for better personalization","display_name":"Multi-center federated learning: clients clustering for better personalization","publication_year":2022,"publication_date":"2022-06-09","ids":{"openalex":"https://openalex.org/W4281753687","doi":"https://doi.org/10.1007/s11280-022-01046-x"},"language":"en","primary_location":{"id":"doi:10.1007/s11280-022-01046-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11280-022-01046-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11280-022-01046-x.pdf","source":{"id":"https://openalex.org/S129236917","display_name":"World Wide Web","issn_l":"1386-145X","issn":["1386-145X","1573-1413"],"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":"World Wide Web","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s11280-022-01046-x.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059227406","display_name":"Guodong Long","orcid":"https://orcid.org/0000-0003-3740-9515"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Guodong Long","raw_affiliation_strings":["Australian AI Institute, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Australian AI Institute, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027093231","display_name":"Ming Xie","orcid":"https://orcid.org/0000-0003-1088-1158"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Ming Xie","raw_affiliation_strings":["Australian AI Institute, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Australian AI Institute, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100611243","display_name":"Tao Shen","orcid":"https://orcid.org/0000-0003-3315-2468"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Tao Shen","raw_affiliation_strings":["Australian AI Institute, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Australian AI Institute, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039076312","display_name":"Tianyi Zhou","orcid":"https://orcid.org/0000-0001-5348-0632"},"institutions":[{"id":"https://openalex.org/I201448701","display_name":"University of Washington","ror":"https://ror.org/00cvxb145","country_code":"US","type":"education","lineage":["https://openalex.org/I201448701"]},{"id":"https://openalex.org/I66946132","display_name":"University of Maryland, College Park","ror":"https://ror.org/047s2c258","country_code":"US","type":"education","lineage":["https://openalex.org/I66946132"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tianyi Zhou","raw_affiliation_strings":["University of Maryland, Maryland, MD, USA","University of Washington, Seattle, WA, USA"],"affiliations":[{"raw_affiliation_string":"University of Maryland, Maryland, MD, USA","institution_ids":["https://openalex.org/I66946132"]},{"raw_affiliation_string":"University of Washington, Seattle, WA, USA","institution_ids":["https://openalex.org/I201448701"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076107706","display_name":"Xianzhi Wang","orcid":"https://orcid.org/0000-0001-9582-3445"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xianzhi Wang","raw_affiliation_strings":["Australian AI Institute, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Australian AI Institute, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057139422","display_name":"Jing Jiang","orcid":"https://orcid.org/0000-0001-5301-7779"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jing Jiang","raw_affiliation_strings":["Australian AI Institute, University of Technology Sydney, Sydney, Australia"],"affiliations":[{"raw_affiliation_string":"Australian AI Institute, University of Technology Sydney, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5059227406"],"corresponding_institution_ids":["https://openalex.org/I114017466"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":34.7075,"has_fulltext":true,"cited_by_count":263,"citation_normalized_percentile":{"value":0.99799268,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"26","issue":"1","first_page":"481","last_page":"500"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9997000098228455,"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":0.9997000098228455,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9948999881744385,"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"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9781000018119812,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.9038865566253662},{"id":"https://openalex.org/keywords/personalization","display_name":"Personalization","score":0.7401732802391052},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.70112544298172},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6590893268585205},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5823233127593994},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4984397888183594},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4896757900714874},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.44881266355514526},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.41455233097076416},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38219571113586426},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32832813262939453},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.11446976661682129}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.9038865566253662},{"id":"https://openalex.org/C183003079","wikidata":"https://www.wikidata.org/wiki/Q1000371","display_name":"Personalization","level":2,"score":0.7401732802391052},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.70112544298172},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6590893268585205},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5823233127593994},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4984397888183594},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4896757900714874},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.44881266355514526},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.41455233097076416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38219571113586426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32832813262939453},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11446976661682129},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s11280-022-01046-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11280-022-01046-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11280-022-01046-x.pdf","source":{"id":"https://openalex.org/S129236917","display_name":"World Wide Web","issn_l":"1386-145X","issn":["1386-145X","1573-1413"],"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":"World Wide Web","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2108.08647","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2108.08647","pdf_url":"https://arxiv.org/pdf/2108.08647","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":null,"raw_type":"text"}],"best_oa_location":{"id":"doi:10.1007/s11280-022-01046-x","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s11280-022-01046-x","pdf_url":"https://link.springer.com/content/pdf/10.1007/s11280-022-01046-x.pdf","source":{"id":"https://openalex.org/S129236917","display_name":"World Wide Web","issn_l":"1386-145X","issn":["1386-145X","1573-1413"],"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":"World Wide Web","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.4300000071525574},{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4099999964237213}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320967","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4281753687.pdf","grobid_xml":"https://content.openalex.org/works/W4281753687.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W1530276735","https://openalex.org/W1686266550","https://openalex.org/W1834627138","https://openalex.org/W1981276685","https://openalex.org/W2072405662","https://openalex.org/W2145360759","https://openalex.org/W2620512600","https://openalex.org/W2734358244","https://openalex.org/W2747810234","https://openalex.org/W2912213068","https://openalex.org/W2963334472","https://openalex.org/W2995022099","https://openalex.org/W3012501605","https://openalex.org/W3012549930","https://openalex.org/W3013860853","https://openalex.org/W3015636663","https://openalex.org/W3020872118","https://openalex.org/W3035855269","https://openalex.org/W3035937838","https://openalex.org/W3086590218","https://openalex.org/W3094605956","https://openalex.org/W3100779497","https://openalex.org/W3102998633","https://openalex.org/W3107100345","https://openalex.org/W3108634112","https://openalex.org/W3109695251","https://openalex.org/W3119350139","https://openalex.org/W3134761328","https://openalex.org/W3168256142","https://openalex.org/W3196053097","https://openalex.org/W3209485964","https://openalex.org/W4212774754","https://openalex.org/W4283796083","https://openalex.org/W4285606214","https://openalex.org/W6600047755","https://openalex.org/W6600076646","https://openalex.org/W6600100092","https://openalex.org/W6600140940","https://openalex.org/W6600146492","https://openalex.org/W6600228633","https://openalex.org/W6600424091","https://openalex.org/W6600512042","https://openalex.org/W6602046083","https://openalex.org/W6605001883","https://openalex.org/W6607687417","https://openalex.org/W6609821937","https://openalex.org/W6609906364","https://openalex.org/W6630944157","https://openalex.org/W6636221917","https://openalex.org/W6743119038","https://openalex.org/W6837536843"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2383111961","https://openalex.org/W2365952365","https://openalex.org/W2352448290","https://openalex.org/W2380820513","https://openalex.org/W4321353415","https://openalex.org/W2913146933","https://openalex.org/W2745001401","https://openalex.org/W2372385138","https://openalex.org/W4303448918"],"abstract_inverted_index":{"Abstract":[0],"Personalized":[1],"decision-making":[2],"can":[3,13,30,179],"be":[4,180],"implemented":[5],"in":[6,56,66,130],"a":[7,16,81,107,137,184],"Federated":[8],"learning":[9],"(FL)":[10],"framework":[11],"that":[12,62,178,198],"collaboratively":[14],"train":[15],"decision":[17,68],"model":[18,84],"by":[19,93,183],"extracting":[20],"knowledge":[21,89],"across":[22,117],"intelligent":[23],"clients,":[24],"e.g.":[25],"smartphones":[26],"or":[27],"enterprises.":[28],"FL":[29,53,77,196],"mitigate":[31],"the":[32,57,87,99,115,122,157,163,214],"data":[33,72,103,155],"privacy":[34],"risk":[35],"of":[36,59,90,98,109,195],"collaborative":[37],"training":[38],"since":[39],"it":[40,173],"merely":[41],"collects":[42],"local":[43],"gradients":[44],"from":[45,154],"users":[46,92,167],"without":[47],"access":[48],"to":[49,85,124,142],"their":[50,95,102,146],"data.":[51],"However,":[52],"is":[54,63],"fragile":[55],"presence":[58],"statistical":[60],"heterogeneity":[61,116],"commonly":[64],"encountered":[65],"personalized":[67],"making,":[69],"e.g.,":[70],"non-IID":[71],"over":[73],"different":[74,125],"clients.":[75],"Existing":[76],"approaches":[78],"usually":[79],"update":[80],"single":[82],"global":[83,111,126,152],"capture":[86,114],"shared":[88],"all":[91],"aggregating":[94],"gradients,":[96],"regardless":[97],"discrepancy":[100],"between":[101,166],"distributions.":[104],"By":[105],"comparison,":[106],"mixture":[108],"multiple":[110,151,192],"models":[112,127,153],"could":[113],"various":[118],"clients":[119,144],"if":[120],"assigning":[121],"client":[123],"(i.e.,":[128],"centers)":[129],"FL.":[131],"To":[132],"this":[133],"end,":[134],"we":[135],"propose":[136],"novel":[138],"multi-center":[139],"aggregation":[140],"mechanism":[141],"cluster":[143,158],"using":[145],"models\u2019":[147],"parameters.":[148],"It":[149],"learns":[150],"as":[156,174],"centers,":[159],"and":[160,168],"simultaneously":[161],"derives":[162],"optimal":[164],"matching":[165],"centers.":[169],"We":[170],"then":[171],"formulate":[172],"an":[175],"optimization":[176],"problem":[177],"efficiently":[181],"solved":[182],"stochastic":[185],"expectation":[186],"maximization":[187],"(EM)":[188],"algorithm.":[189],"Experiments":[190],"on":[191,213],"benchmark":[193],"datasets":[194],"show":[197],"our":[199],"method":[200],"outperforms":[201],"several":[202],"popular":[203],"baseline":[204],"methods.":[205],"The":[206],"experimental":[207],"source":[208],"codes":[209],"are":[210],"publicly":[211],"available":[212],"Github":[215],"repository":[216],"(GitHub":[217],"repository:":[218],"https://github.com/mingxuts/multi-center-fed-learning":[219],").":[220]},"counts_by_year":[{"year":2026,"cited_by_count":12},{"year":2025,"cited_by_count":121},{"year":2024,"cited_by_count":67},{"year":2023,"cited_by_count":57},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2022-06-13T00:00:00"}
