{"id":"https://openalex.org/W7117723227","doi":"https://doi.org/10.1109/wincom65874.2025.11313391","title":"Device- and Location-Aware Client Clustering for Heterogeneous Federated Learning","display_name":"Device- and Location-Aware Client Clustering for Heterogeneous Federated Learning","publication_year":2025,"publication_date":"2025-11-25","ids":{"openalex":"https://openalex.org/W7117723227","doi":"https://doi.org/10.1109/wincom65874.2025.11313391"},"language":null,"primary_location":{"id":"doi:10.1109/wincom65874.2025.11313391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wincom65874.2025.11313391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 12th International Conference on Wireless Networks and Mobile Communications (WINCOM)","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/A5105892444","display_name":"Maryam Ben Driss","orcid":"https://orcid.org/0009-0008-4254-9700"},"institutions":[{"id":"https://openalex.org/I159129438","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al","ror":"https://ror.org/002rjbv21","country_code":"CA","type":"education","lineage":["https://openalex.org/I159129438","https://openalex.org/I49663120"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Maryam Ben Driss","raw_affiliation_strings":["University of Quebec at Montreal,Department of Computer Science,Montreal,Quebec,Canada,H2L 2C4"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Quebec at Montreal,Department of Computer Science,Montreal,Quebec,Canada,H2L 2C4","institution_ids":["https://openalex.org/I159129438"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5105611914","display_name":"Essaid Sabir","orcid":null},"institutions":[{"id":"https://openalex.org/I159129438","display_name":"Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al","ror":"https://ror.org/002rjbv21","country_code":"CA","type":"education","lineage":["https://openalex.org/I159129438","https://openalex.org/I49663120"]},{"id":"https://openalex.org/I200745827","display_name":"Universit\u00e9 T\u00c9LUQ","ror":"https://ror.org/007y6q934","country_code":"CA","type":"education","lineage":["https://openalex.org/I200745827","https://openalex.org/I49663120"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Essaid Sabir","raw_affiliation_strings":["TELUQ, University of Quebec,Department of Science and Technology,Montreal,Canada,H2S 3L4"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TELUQ, University of Quebec,Department of Science and Technology,Montreal,Canada,H2S 3L4","institution_ids":["https://openalex.org/I200745827","https://openalex.org/I159129438"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.78461764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9510999917984009,"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":0.9510999917984009,"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/T10273","display_name":"IoT and Edge/Fog Computing","score":0.007600000128149986,"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/T14347","display_name":"Big Data and Digital Economy","score":0.005799999926239252,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7215999960899353},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.6759999990463257},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5895000100135803},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5782999992370605},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5605999827384949},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.5577999949455261},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.48730000853538513},{"id":"https://openalex.org/keywords/efficient-energy-use","display_name":"Efficient energy use","score":0.42559999227523804}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526999711990356},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7215999960899353},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.6759999990463257},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5895000100135803},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5782999992370605},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5605999827384949},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.5577999949455261},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.48730000853538513},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.42559999227523804},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4147000014781952},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2888999879360199},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C2775973920","wikidata":"https://www.wikidata.org/wiki/Q3252726","display_name":"Selection algorithm","level":3,"score":0.2773999869823456},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.274399995803833},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C70061542","wikidata":"https://www.wikidata.org/wiki/Q989016","display_name":"Distributed database","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.26080000400543213},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.25519999861717224},{"id":"https://openalex.org/C158207573","wikidata":"https://www.wikidata.org/wiki/Q5747224","display_name":"Heterogeneous network","level":4,"score":0.25380000472068787}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wincom65874.2025.11313391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wincom65874.2025.11313391","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 12th International Conference on Wireless Networks and Mobile Communications (WINCOM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.9118671417236328}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2975156709","https://openalex.org/W3080934299","https://openalex.org/W3158567365","https://openalex.org/W4226348979","https://openalex.org/W4285504041","https://openalex.org/W4385338519","https://openalex.org/W4393928221","https://openalex.org/W4399141804","https://openalex.org/W4405114091"],"related_works":[],"abstract_inverted_index":{"The":[0,124],"exponential":[1],"increase":[2,170],"in":[3,18,99,140,150,156,163,171],"decentralized":[4],"data":[5,55,75],"generated":[6],"by":[7],"heterogeneous":[8],"devices":[9],"presents":[10],"critical":[11,182],"challenges":[12],"for":[13,194],"federated":[14],"learning":[15],"(FL),":[16],"particularly":[17],"handling":[19],"nonindependent":[20],"and":[21,29,45,54,80,105,116,166,188,197],"identically":[22],"distributed":[23],"(non-IID)":[24],"data,":[25],"limited":[26],"client":[27,52,95,118,192],"resources,":[28],"the":[30,181],"preservation":[31],"of":[32,184],"privacy.":[33],"Traditional":[34],"FL":[35,64,199],"approaches":[36],"often":[37],"suffer":[38],"from":[39],"prolonged":[40],"training":[41,157],"times,":[42],"reduced":[43],"accuracy,":[44,104,151],"high":[46],"energy":[47,107,172],"consumption":[48],"due":[49],"to":[50,83,131,175],"uncoordinated":[51],"participation":[53],"diversity.":[56],"In":[57],"this":[58,89],"paper,":[59],"we":[60],"propose":[61],"an":[62],"advanced":[63],"framework":[65,93],"that":[66],"strategically":[67],"clusters":[68],"clients":[69],"based":[70],"on":[71],"multiple":[72],"attributes,":[73],"including":[74,129],"distribution":[76],"patterns,":[77],"device":[78,186],"types,":[79],"geographic":[81,189],"locations,":[82],"address":[84],"these":[85],"challenges.":[86],"By":[87],"leveraging":[88],"multi-dimensional":[90],"clustering,":[91],"our":[92,111],"optimizes":[94],"selection":[96,119],"processes,":[97],"resulting":[98],"faster":[100],"convergence,":[101],"improved":[102],"model":[103,141],"enhanced":[106],"efficiency.":[108],"We":[109],"benchmark":[110],"approach":[112],"against":[113],"random,":[114],"roundrobin,":[115],"accuracy-based":[117],"strategies":[120],"through":[121],"extensive":[122],"experiments.":[123],"results":[125],"demonstrate":[126],"notable":[127],"improvements,":[128],"up":[130],"a":[132,143,152,159,167],"<tex":[133,144],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[134,145],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{1.":[135],"0":[136],"9":[137],"\\%}$</tex>":[138,148],"decrease":[139],"loss,":[142],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{2.":[146],"8":[147],"boost":[149],"20.6":[153],"%":[154,161,169],"reduction":[155],"duration,":[158],"30.8":[160],"cut":[162],"communication":[164],"overhead,":[165],"25.5":[168],"efficiency":[173],"compared":[174],"baseline":[176],"methods.":[177],"Our":[178],"findings":[179],"highlight":[180],"role":[183],"incorporating":[185],"heterogeneity":[187],"context":[190],"into":[191],"clustering":[193],"scalable,":[195],"efficient,":[196],"privacy-preserving":[198],"deployments.":[200]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-12-31T00:00:00"}
