{"id":"https://openalex.org/W7138898506","doi":"https://doi.org/10.1109/globecom59602.2025.11432810","title":"Towards Heterogeneity-Free: Client Selection for Federated Learning with Prototype Margin","display_name":"Towards Heterogeneity-Free: Client Selection for Federated Learning with Prototype Margin","publication_year":2025,"publication_date":"2025-12-08","ids":{"openalex":"https://openalex.org/W7138898506","doi":"https://doi.org/10.1109/globecom59602.2025.11432810"},"language":null,"primary_location":{"id":"doi:10.1109/globecom59602.2025.11432810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","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/A5129861243","display_name":"Huaye Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Huaye Zhang","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129846177","display_name":"Yuan Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Liu","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China"],"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering,Guangzhou,China","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5129861243"],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.88118339,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1365","last_page":"1370"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.910099983215332,"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":0.910099983215332,"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/T13918","display_name":"Advanced Data and IoT Technologies","score":0.01600000075995922,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.006500000134110451,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.8145999908447266},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6940000057220459},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.626800000667572},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.5918999910354614},{"id":"https://openalex.org/keywords/wireless","display_name":"Wireless","score":0.48910000920295715},{"id":"https://openalex.org/keywords/bandwidth","display_name":"Bandwidth (computing)","score":0.4738999903202057},{"id":"https://openalex.org/keywords/wireless-network","display_name":"Wireless network","score":0.4611999988555908},{"id":"https://openalex.org/keywords/client-side","display_name":"Client-side","score":0.43160000443458557}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8349999785423279},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.8145999908447266},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6940000057220459},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.626800000667572},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.5918999910354614},{"id":"https://openalex.org/C555944384","wikidata":"https://www.wikidata.org/wiki/Q249","display_name":"Wireless","level":2,"score":0.48910000920295715},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.4738999903202057},{"id":"https://openalex.org/C108037233","wikidata":"https://www.wikidata.org/wiki/Q11375","display_name":"Wireless network","level":3,"score":0.4611999988555908},{"id":"https://openalex.org/C202477664","wikidata":"https://www.wikidata.org/wiki/Q1352449","display_name":"Client-side","level":2,"score":0.43160000443458557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4074999988079071},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.4004000127315521},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.38359999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.382999986410141},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.375},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.3253999948501587},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.31690001487731934},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3140999972820282},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C152880691","wikidata":"https://www.wikidata.org/wiki/Q146813","display_name":"Client\u2013server model","level":3,"score":0.2992999851703644},{"id":"https://openalex.org/C24756922","wikidata":"https://www.wikidata.org/wiki/Q1757694","display_name":"Data quality","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2669000029563904},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globecom59602.2025.11432810","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globecom59602.2025.11432810","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"GLOBECOM 2025 - 2025 IEEE Global Communications Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2068728923","https://openalex.org/W2798720628","https://openalex.org/W2977678469","https://openalex.org/W3015636663","https://openalex.org/W3046167303","https://openalex.org/W3059999910","https://openalex.org/W3086809868","https://openalex.org/W3163105316","https://openalex.org/W4226183928","https://openalex.org/W4319654190","https://openalex.org/W4319988669","https://openalex.org/W4385819926","https://openalex.org/W4389371065","https://openalex.org/W4396240126"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"has":[3],"gained":[4],"significant":[5,34],"attention":[6],"as":[7],"a":[8,33,44],"distributed":[9],"machine":[10],"learning":[11],"paradigm,":[12],"leveraging":[13],"edge":[14],"clients":[15,90],"for":[16,73,84,120],"collaborative":[17],"training":[18,109],"while":[19],"preserving":[20],"data":[21,30,65,75],"privacy.":[22],"However,":[23],"statistical":[24],"heterogeneity,":[25],"arising":[26],"from":[27],"diverse":[28],"client":[29,47,68,114,126],"distributions,":[31],"poses":[32],"challenge":[35],"to":[36,54,70],"FL":[37],"performance.":[38],"In":[39],"this":[40],"work,":[41],"we":[42,62,94,111],"propose":[43],"novel":[45],"heterogeneity-aware":[46],"selection":[48,69,115],"scheme":[49,81],"based":[50,97],"on":[51,98],"prototype":[52,60,100],"margin":[53,101],"mitigate":[55],"these":[56],"challenges.":[57],"By":[58],"utilizing":[59],"learning,":[61],"capture":[63],"effective":[64],"representations,":[66],"allowing":[67],"better":[71],"account":[72],"both":[74],"quality":[76],"and":[77,93,102,116,124,138],"distribution.":[78],"The":[79],"proposed":[80],"is":[82],"designed":[83],"practical":[85],"wireless":[86,103],"communication":[87,140],"networks,":[88],"where":[89],"are":[91],"clustered":[92],"select":[95],"participants":[96],"joint":[99],"channel.":[104],"To":[105],"further":[106],"optimize":[107,113],"the":[108],"process,":[110],"jointly":[112],"bandwidth":[117],"allocation,":[118],"accounting":[119],"dynamic":[121],"network":[122],"conditions":[123],"heterogeneous":[125],"capabilities.":[127],"Experimental":[128],"results":[129],"demonstrate":[130],"that":[131],"our":[132],"approach":[133],"significantly":[134],"improves":[135],"model":[136],"accuracy":[137],"reduces":[139],"overhead.":[141]},"counts_by_year":[],"updated_date":"2026-03-20T20:54:20.808490","created_date":"2026-03-20T00:00:00"}