{"id":"https://openalex.org/W4412610564","doi":"https://doi.org/10.1109/hpcc64274.2024.00069","title":"DRCFL: Representation Driven Head Clustering for Federated Learning on Edge Devices","display_name":"DRCFL: Representation Driven Head Clustering for Federated Learning on Edge Devices","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4412610564","doi":"https://doi.org/10.1109/hpcc64274.2024.00069"},"language":"en","primary_location":{"id":"doi:10.1109/hpcc64274.2024.00069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00069","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 High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5072499855","display_name":"Liyu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liyu Wang","raw_affiliation_strings":["Hubei University,School of Computer Science and Information Engineering,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University,School of Computer Science and Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006071665","display_name":"Zheyu Yang","orcid":"https://orcid.org/0000-0001-9425-1676"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheyu Yang","raw_affiliation_strings":["Hubei University,School of Computer Science and Information Engineering,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University,School of Computer Science and Information Engineering,Wuhan,China","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100397616","display_name":"Yang Yang","orcid":"https://orcid.org/0000-0002-5070-4511"},"institutions":[{"id":"https://openalex.org/I75900474","display_name":"Hubei University","ror":"https://ror.org/03a60m280","country_code":"CN","type":"education","lineage":["https://openalex.org/I75900474"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":["Hubei University,Key Laboratory of Intelligent Sensing System and Security (Ministry of Education), School of Artificial Intelligence,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hubei University,Key Laboratory of Intelligent Sensing System and Security (Ministry of Education), School of Artificial Intelligence,Wuhan,China","institution_ids":["https://openalex.org/I75900474"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113230916","display_name":"Bin Luo","orcid":"https://orcid.org/0009-0006-8247-8199"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Luo","raw_affiliation_strings":["State Grid Hubei Electric Power Co., Ltd.,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Hubei Electric Power Co., Ltd.,Wuhan,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100407786","display_name":"Wenfeng Xu","orcid":"https://orcid.org/0000-0001-5248-9467"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenfeng Xu","raw_affiliation_strings":["State Grid Hubei Electric Power Co., Ltd.,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Hubei Electric Power Co., Ltd.,Wuhan,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107857964","display_name":"Xiaodong Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiaodong Yu","raw_affiliation_strings":["State Grid Hubei Electric Power Co., Ltd.,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Grid Hubei Electric Power Co., Ltd.,Wuhan,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110589442","display_name":"Linlin Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100789","display_name":"Wuhan College","ror":"https://ror.org/01dashf18","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4210100789"]},{"id":"https://openalex.org/I4400573219","display_name":"Wenhua College","ror":"https://ror.org/05fagpw72","country_code":null,"type":"education","lineage":["https://openalex.org/I4400573219"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linlin Zhu","raw_affiliation_strings":["Wenhua College,Department of Information Science and Technology,Wuhan,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Wenhua College,Department of Information Science and Technology,Wuhan,China","institution_ids":["https://openalex.org/I4210100789","https://openalex.org/I4400573219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3556,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63936804,"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":"466","last_page":"473"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9951000213623047,"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":0.9951000213623047,"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/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9003000259399414,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"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":0.7762286067008972},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7378590106964111},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6870108246803284},{"id":"https://openalex.org/keywords/head","display_name":"Head (geology)","score":0.6600487232208252},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5936002731323242},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4602805972099304},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4184894263744354},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.04726690053939819}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7762286067008972},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7378590106964111},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6870108246803284},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.6600487232208252},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5936002731323242},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4602805972099304},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4184894263744354},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.04726690053939819},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcc64274.2024.00069","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00069","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 High Performance Computing and Communications (HPCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320313620","display_name":"Hubei Provincial Department of Education","ror":"https://ror.org/05yaa9j15"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321408","display_name":"Ministry of Education","ror":"https://ror.org/01p262204"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W2963512530","https://openalex.org/W2979637109","https://openalex.org/W3007720613","https://openalex.org/W3018464563","https://openalex.org/W3047304572","https://openalex.org/W3080934299","https://openalex.org/W3091635927","https://openalex.org/W3104631511","https://openalex.org/W3156024711","https://openalex.org/W3164715504","https://openalex.org/W3187661226","https://openalex.org/W4200631596","https://openalex.org/W4212807025","https://openalex.org/W4283796083","https://openalex.org/W4381785392","https://openalex.org/W4382317695","https://openalex.org/W4382404965","https://openalex.org/W4387969057","https://openalex.org/W4388666079","https://openalex.org/W4390492901","https://openalex.org/W4390872538","https://openalex.org/W4390873050","https://openalex.org/W4392152297","https://openalex.org/W4407576239","https://openalex.org/W6728757088","https://openalex.org/W6752029299","https://openalex.org/W6755988804","https://openalex.org/W6768632158","https://openalex.org/W6770590064","https://openalex.org/W6772318479","https://openalex.org/W6774978782","https://openalex.org/W6779174293","https://openalex.org/W6784336702","https://openalex.org/W6784971290","https://openalex.org/W6786597537","https://openalex.org/W6787972765","https://openalex.org/W6791102956","https://openalex.org/W6791189488","https://openalex.org/W6791444617","https://openalex.org/W6796484261","https://openalex.org/W6796504275","https://openalex.org/W6810249531","https://openalex.org/W6854708048","https://openalex.org/W6862857194"],"related_works":["https://openalex.org/W4298130764","https://openalex.org/W3097502728","https://openalex.org/W2804364458","https://openalex.org/W2132641928","https://openalex.org/W4310225030","https://openalex.org/W2090259340","https://openalex.org/W1926736923","https://openalex.org/W2158836806","https://openalex.org/W2393816671","https://openalex.org/W2021673619"],"abstract_inverted_index":{"Clustered":[0],"Federated":[1,65],"Learning":[2,66],"(CFL)":[3],"effectively":[4],"mitigates":[5],"the":[6,37,49,71,91,112,119,124,134],"negative":[7],"impact":[8],"of":[9,31,51,136,141,153],"data":[10,22,88,109,127],"heterogeneity":[11],"among":[12],"clients":[13,19,35],"on":[14,123],"model":[15,32,72],"accuracy":[16],"by":[17],"grouping":[18],"with":[20],"similar":[21],"distributions":[23],"for":[24,64,101],"training.":[25],"However,":[26],"existing":[27],"methods":[28],"require":[29],"communication":[30,42,116,142],"parameters":[33],"between":[34],"and":[36,79,96],"server,":[38,113],"leading":[39],"to":[40,107,111,156],"high":[41],"costs.":[43,117],"Additionally,":[44,118],"current":[45],"clustering":[46],"strategies":[47],"restrict":[48],"flexibility":[50,135],"cluster":[52,137],"partitioning.":[53],"To":[54],"address":[55],"these":[56,94],"issues,":[57],"we":[58],"propose":[59],"a":[60,74,80,98,151],"Dynamic":[61],"Representation":[62,75],"Clustering":[63],"(DRCFL)":[67],"algorithm.":[68],"It":[69],"decouples":[70],"into":[73],"Generation":[76],"Module":[77],"(RGM)":[78],"Shared":[81],"header":[82,100],"(S-header).":[83],"The":[84],"former":[85],"calculates":[86],"client":[87],"representations,":[89],"while":[90],"server":[92,120],"clusters":[93],"representations":[95,110,128],"trains":[97],"shared":[99],"each":[102,130],"cluster.":[103],"Clients":[104],"only":[105],"need":[106],"transmit":[108],"remarkably":[114],"reducing":[115],"re-clusters":[121],"based":[122],"most":[125],"recent":[126],"in":[129],"training":[131],"round,":[132],"enhancing":[133],"partitions.":[138],"In":[139],"terms":[140],"cost,":[143],"Extensive":[144],"experimental":[145],"results":[146],"indicate":[147],"that":[148],"DRCFL":[149],"achieves":[150],"reduction":[152],"22.70%\u221283.33%":[154],"compared":[155],"state-of-the-art":[157],"(SOTA)":[158],"personalized":[159],"FL":[160],"methods.":[161]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
