{"id":"https://openalex.org/W4412610606","doi":"https://doi.org/10.1109/hpcc64274.2024.00075","title":"FedMHC: Overcoming Dimensionality and Communication Challenges for Personalized Federated Learning Using Model Head Clustering","display_name":"FedMHC: Overcoming Dimensionality and Communication Challenges for Personalized Federated Learning Using Model Head Clustering","publication_year":2024,"publication_date":"2024-12-13","ids":{"openalex":"https://openalex.org/W4412610606","doi":"https://doi.org/10.1109/hpcc64274.2024.00075"},"language":"en","primary_location":{"id":"doi:10.1109/hpcc64274.2024.00075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00075","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/A5101847039","display_name":"Haotian Zheng","orcid":"https://orcid.org/0000-0003-2056-691X"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Zheng","raw_affiliation_strings":["Hohai University,School of Computer and Information,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,School of Computer and Information,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113881053","display_name":"Yingchi Mao","orcid":"https://orcid.org/0000-0002-9884-8100"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingchi Mao","raw_affiliation_strings":["Hohai University,School of Computer and Information,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,School of Computer and Information,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018883565","display_name":"Haowen Xu","orcid":"https://orcid.org/0000-0002-2309-9176"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haowen Xu","raw_affiliation_strings":["Hohai University,School of Computer and Information,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,School of Computer and Information,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068227459","display_name":"Xiaoming He","orcid":"https://orcid.org/0000-0003-4196-3041"},"institutions":[{"id":"https://openalex.org/I41198531","display_name":"Nanjing University of Posts and Telecommunications","ror":"https://ror.org/043bpky34","country_code":"CN","type":"education","lineage":["https://openalex.org/I41198531"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming He","raw_affiliation_strings":["Nanjing University of Posts and Telecommunications,College of Internet of Things,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Posts and Telecommunications,College of Internet of Things,Nanjing,China","institution_ids":["https://openalex.org/I41198531"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111027049","display_name":"Benteng Zhang","orcid":"https://orcid.org/0009-0006-6946-5254"},"institutions":[{"id":"https://openalex.org/I163340411","display_name":"Hohai University","ror":"https://ror.org/01wd4xt90","country_code":"CN","type":"education","lineage":["https://openalex.org/I163340411"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Benteng Zhang","raw_affiliation_strings":["Hohai University,School of Computer and Information,Nanjing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hohai University,School of Computer and Information,Nanjing,China","institution_ids":["https://openalex.org/I163340411"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090590984","display_name":"Feng Mao","orcid":"https://orcid.org/0000-0001-6171-3168"},"institutions":[{"id":"https://openalex.org/I2800271857","display_name":"Sumco Corporation (Japan)","ror":"https://ror.org/01yp72g57","country_code":"JP","type":"company","lineage":["https://openalex.org/I2800271857"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Feng Mao","raw_affiliation_strings":["Suma Technology Co., Ltd,Research and Development Department,Suzhou,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Suma Technology Co., Ltd,Research and Development Department,Suzhou,China","institution_ids":["https://openalex.org/I2800271857"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046861518","display_name":"Jie Wu","orcid":"https://orcid.org/0000-0001-9531-7817"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jie Wu","raw_affiliation_strings":["Temple University,Center of Networked Computing,Philadelphia,PA,U.S.A"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Temple University,Center of Networked Computing,Philadelphia,PA,U.S.A","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3355,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.6277068,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"516","last_page":"521"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9821000099182129,"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":0.9821000099182129,"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":0.8025744557380676},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.7302579283714294},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5894202589988708},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.5568910241127014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44595104455947876},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.36071622371673584}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8025744557380676},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7302579283714294},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5894202589988708},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.5568910241127014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44595104455947876},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36071622371673584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/hpcc64274.2024.00075","is_oa":false,"landing_page_url":"https://doi.org/10.1109/hpcc64274.2024.00075","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/F4320337504","display_name":"Research and Development","ror":"https://ror.org/027s68j25"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W3080934299","https://openalex.org/W3104631511","https://openalex.org/W4281753687","https://openalex.org/W4316663445","https://openalex.org/W4318755628","https://openalex.org/W4386260712","https://openalex.org/W4387105517","https://openalex.org/W4388145633","https://openalex.org/W6685053522","https://openalex.org/W6728757088","https://openalex.org/W6741978826","https://openalex.org/W6750254146","https://openalex.org/W6759238902","https://openalex.org/W6763048141","https://openalex.org/W6776730200","https://openalex.org/W6779174293","https://openalex.org/W6787972765","https://openalex.org/W6791102956","https://openalex.org/W6791444617","https://openalex.org/W6862857194"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"In":[0],"real":[1],"Internet":[2],"of":[3,158],"Things":[4],"(IoT)":[5],"environments,":[6],"IoT":[7,16,46,84,101,149],"devices":[8,17,85],"vary":[9],"widely":[10],"in":[11,19,56,183,193],"data":[12,29,88,112],"types":[13],"and":[14,52,62,90,114,139,160,168,181,187,191],"needs.":[15],"participating":[18],"Personalized":[20,39,71],"Federated":[21,72],"Learning":[22,73],"(PFL)":[23],"all":[24],"have":[25],"their":[26],"own":[27],"unique":[28],"characteristics,":[30],"but":[31],"there":[32],"exist":[33],"some":[34],"similarities.":[35],"Previous":[36],"work":[37],"enhances":[38,175],"Local":[40],"Models":[41],"(PLMs)\u2019":[42],"performance":[43],"by":[44,179,188],"clustering":[45],"devices\u2019":[47],"PLM":[48,116],"while":[49],"ignoring":[50],"dimensionality":[51,159],"communication":[53,147,162],"volume,":[54],"resulting":[55],"lower":[57],"Global":[58],"Model":[59,77],"(GM)":[60],"accuracy":[61,178],"PLMs\u2019":[63],"performance.":[64],"To":[65],"this":[66],"end,":[67],"we":[68],"propose":[69],"a":[70,105,140],"method":[74],"based":[75],"on":[76,124,165],"Head":[78],"Clustering":[79],"(FedMHC).":[80],"Specifically,":[81],"FedMHC":[82,98,118,174],"groups":[83],"with":[86,148],"similar":[87],"characteristics":[89,113],"distributes":[91],"different":[92,95],"GMs":[93],"to":[94,103,134,198],"device":[96,102],"groups.":[97],"allows":[99],"each":[100],"obtain":[104],"GM":[106],"that":[107,173],"best":[108],"fits":[109],"its":[110],"local":[111],"guides":[115],"training.":[117],"only":[119,132],"clusters":[120],"model":[121,177],"head":[122,137],"parameters":[123,138,145],"the":[125,129,136,156,176,199],"server":[126,131],"side.":[127],"Thus,":[128],"edge":[130],"needs":[133],"transmit":[135],"single":[141],"shared":[142],"feature":[143],"extractor":[144],"during":[146],"devices.":[150],"The":[151],"improvement":[152],"can":[153],"effectively":[154],"address":[155],"issues":[157],"high":[161],"volume.":[163],"Experiments":[164],"CIFAR-100,":[166],"Tiny-ImageNet,":[167],"AG":[169],"News":[170],"datasets":[171],"demonstrate":[172],"1.79%":[180],"5.9%":[182],"pathological":[184],"heterogeneous":[185,195],"scenarios,":[186,196],"1.43%,":[189],"0.92%,":[190],"0.94%":[192],"practical":[194],"compared":[197],"topperforming":[200],"methods":[201],"among":[202],"9":[203],"baselines.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-11T07:18:39.950985","created_date":"2025-10-10T00:00:00"}