{"id":"https://openalex.org/W4403407116","doi":"https://doi.org/10.1109/bigdata62323.2024.10825389","title":"Discovering Communities With Clustered Federated Learning","display_name":"Discovering Communities With Clustered Federated Learning","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4403407116","doi":"https://doi.org/10.1109/bigdata62323.2024.10825389"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-04696543/document","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077617465","display_name":"Micka\u00ebl Bettinelli","orcid":null},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Micka\u00ebl Bettinelli","raw_affiliation_strings":["Univ. Savoie Mont Blanc,LISTIC,Annecy,France"],"affiliations":[{"raw_affiliation_string":"Univ. Savoie Mont Blanc,LISTIC,Annecy,France","institution_ids":["https://openalex.org/I70900168"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067804280","display_name":"Alexandre Beno\u00eet","orcid":"https://orcid.org/0000-0002-0627-4948"},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Alexandre Benoit","raw_affiliation_strings":["Univ. Savoie Mont Blanc,LISTIC,Annecy,France"],"affiliations":[{"raw_affiliation_string":"Univ. Savoie Mont Blanc,LISTIC,Annecy,France","institution_ids":["https://openalex.org/I70900168"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5114269042","display_name":"K\u00e9vin Grandjean","orcid":null},"institutions":[{"id":"https://openalex.org/I70900168","display_name":"Universit\u00e9 Savoie Mont Blanc","ror":"https://ror.org/04gqg1a07","country_code":"FR","type":"education","lineage":["https://openalex.org/I70900168"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"K\u00e9vin Grandjean","raw_affiliation_strings":["Univ. Savoie Mont Blanc,LISTIC,Annecy,France"],"affiliations":[{"raw_affiliation_string":"Univ. Savoie Mont Blanc,LISTIC,Annecy,France","institution_ids":["https://openalex.org/I70900168"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5077617465"],"corresponding_institution_ids":["https://openalex.org/I70900168"],"apc_list":null,"apc_paid":null,"fwci":0.3544,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.67675915,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7648","last_page":"7657"},"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/T10796","display_name":"Cooperative Communication and Network Coding","score":0.9868999719619751,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.982200026512146,"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":0.7427600622177124},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4398004412651062},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.40877360105514526}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7427600622177124},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4398004412651062},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.40877360105514526}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825389","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825389","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-04696543v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04696543v1/document","pdf_url":"https://hal.science/hal-04696543/document","source":{"id":"https://openalex.org/S4406922454","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024","raw_type":"Preprints, Working Papers, ..."}],"best_oa_location":{"id":"pmh:oai:HAL:hal-04696543v1","is_oa":true,"landing_page_url":"https://hal.science/hal-04696543v1/document","pdf_url":"https://hal.science/hal-04696543/document","source":{"id":"https://openalex.org/S4406922454","display_name":"SPIRE - Sciences Po Institutional REpository","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2024","raw_type":"Preprints, Working Papers, ..."},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4403407116.pdf","grobid_xml":"https://content.openalex.org/works/W4403407116.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2006255103","https://openalex.org/W2123745357","https://openalex.org/W2131681506","https://openalex.org/W2399495295","https://openalex.org/W2604738573","https://openalex.org/W2794045529","https://openalex.org/W2897249806","https://openalex.org/W2972570881","https://openalex.org/W3004456744","https://openalex.org/W3080934299","https://openalex.org/W3091404410","https://openalex.org/W3091635927","https://openalex.org/W3099768174","https://openalex.org/W3104631511","https://openalex.org/W3106188259","https://openalex.org/W3133814152","https://openalex.org/W3193589553","https://openalex.org/W4297775537","https://openalex.org/W4312970728","https://openalex.org/W4318619660","https://openalex.org/W4376167570","https://openalex.org/W4390824398","https://openalex.org/W6728757088","https://openalex.org/W6737664043","https://openalex.org/W6761472960","https://openalex.org/W6767676916","https://openalex.org/W6773641948","https://openalex.org/W6787972765","https://openalex.org/W6795281715","https://openalex.org/W6850086144","https://openalex.org/W6852514016","https://openalex.org/W6862341203"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"This":[0],"research":[1,223],"addresses":[2],"the":[3,47,54,86,107,114,199,214,227],"challenge":[4],"of":[5,49,75,106,116,201,209],"community":[6,194],"detection":[7],"in":[8,205],"federated":[9,185],"learning":[10,186],"environments":[11],"where":[12],"data":[13,117],"is":[14,44,78],"non-independent":[15],"and":[16,63,128,147,175,188,193],"identically":[17],"distributed":[18],"across":[19],"clients.":[20,71],"We":[21],"propose":[22],"a":[23,58,64,103],"CFL":[24,154],"(Clustered":[25],"Federated":[26],"Learning)":[27],"approach":[28,77,162,180,204],"that":[29,224],"groups":[30],"clients":[31,133],"into":[32],"communities":[33,84,208],"based":[34,45],"on":[35,46,156],"their":[36],"model":[37,59,121,145,177],"similarities":[38],"during":[39],"training.":[40],"The":[41,72,96,179,196],"proposed":[42,228],"method":[43],"integration":[48],"three":[50],"fundamental":[51],"elements,":[52],"namely:":[53],"Louvain":[55],"clustering":[56,148,167,203],"algorithm,":[57],"similarity":[60],"measurement":[61],"system,":[62],"strategy":[65],"for":[66,88,131,221],"attributing":[67],"aggregated":[68],"models":[69],"to":[70,81,101,110,134,152],"primary":[73],"benefit":[74],"this":[76],"its":[79],"capacity":[80],"discern":[82],"client":[83,125,171],"without":[85],"need":[87],"pre-existing":[89],"information,":[90],"while":[91],"simultaneously":[92],"enhancing":[93],"task":[94,191],"performance.":[95],"Cifar10":[97],"dataset":[98],"was":[99],"used":[100],"conduct":[102],"comprehensive":[104],"analysis":[105],"method\u2019s":[108],"response":[109],"various":[111],"factors,":[112],"including":[113],"degree":[115],"distribution":[118],"imbalances,":[119],"different":[120,129],"initialization":[122],"approaches,":[123],"varying":[124],"participation":[126],"rates,":[127],"strategies":[130],"assigning":[132],"clusters.":[135],"Our":[136],"evaluation":[137],"extends":[138],"beyond":[139],"traditional":[140],"metrics":[141],"by":[142],"encompassing":[143],"both":[144,190],"accuracy":[146],"quality.":[149,178],"When":[150],"compared":[151],"existing":[153],"methods":[155],"an":[157],"image":[158],"classification":[159],"problem,":[160],"our":[161,202],"demonstrates":[163],"advantages":[164],"through":[165],"continuous":[166],"throughout":[168],"training,":[169],"flexible":[170],"reassignment":[172],"between":[173],"groups,":[174],"maintained":[176],"integrates":[181],"smoothly":[182],"with":[183],"standard":[184],"frameworks":[187],"improves":[189],"performance":[192],"detection.":[195],"results":[197],"illustrate":[198],"efficacy":[200],"identifying":[206],"relevant":[207],"related":[210],"target":[211],"classes.":[212],"Finally,":[213],"conducted":[215],"experiments":[216],"have":[217],"identified":[218],"specific":[219],"avenues":[220],"further":[222],"will":[225],"extend":[226],"global":[229],"framework.":[230]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}
