{"id":"https://openalex.org/W4408354249","doi":"https://doi.org/10.1109/icassp49660.2025.10889428","title":"LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data","display_name":"LCFed: An Efficient Clustered Federated Learning Framework for Heterogeneous Data","publication_year":2025,"publication_date":"2025-03-12","ids":{"openalex":"https://openalex.org/W4408354249","doi":"https://doi.org/10.1109/icassp49660.2025.10889428"},"language":"en","primary_location":{"id":"doi:10.1109/icassp49660.2025.10889428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100398054","display_name":"Yuxin Zhang","orcid":"https://orcid.org/0009-0008-1907-1788"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuxin Zhang","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102918870","display_name":"Haoyu Chen","orcid":"https://orcid.org/0000-0002-4585-1680"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Chen","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032871667","display_name":"Lin Zheng","orcid":"https://orcid.org/0000-0002-6865-4786"},"institutions":[{"id":"https://openalex.org/I889458895","display_name":"University of Hong Kong","ror":"https://ror.org/02zhqgq86","country_code":"HK","type":"education","lineage":["https://openalex.org/I889458895"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Zheng Lin","raw_affiliation_strings":["The University of Hong Kong,Department of Electrical and Electronic Engineering,Hong Kong,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Hong Kong,Department of Electrical and Electronic Engineering,Hong Kong,China","institution_ids":["https://openalex.org/I889458895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111320775","display_name":"Zhe Chen","orcid":"https://orcid.org/0009-0005-2285-2890"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Chen","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003998216","display_name":"Jin Zhao","orcid":"https://orcid.org/0000-0002-9807-2648"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jin Zhao","raw_affiliation_strings":["Fudan University,School of Computer Science,Shanghai,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,School of Computer Science,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9979000091552734,"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":0.9979000091552734,"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/T10203","display_name":"Recommender Systems and Techniques","score":0.9297000169754028,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9273999929428101,"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":0.7922301292419434},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.4389329254627228},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.35013747215270996},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.28079336881637573}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7922301292419434},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.4389329254627228},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35013747215270996},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.28079336881637573}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp49660.2025.10889428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp49660.2025.10889428","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":25,"referenced_works":["https://openalex.org/W2089468765","https://openalex.org/W2112796928","https://openalex.org/W2204975001","https://openalex.org/W2802548157","https://openalex.org/W2962788286","https://openalex.org/W3080934299","https://openalex.org/W3091635927","https://openalex.org/W4200580682","https://openalex.org/W4206870398","https://openalex.org/W4281753687","https://openalex.org/W4283220690","https://openalex.org/W4283796083","https://openalex.org/W4285600172","https://openalex.org/W4386245250","https://openalex.org/W4391248691","https://openalex.org/W4392909918","https://openalex.org/W4396877837","https://openalex.org/W4400019971","https://openalex.org/W6728757088","https://openalex.org/W6759238902","https://openalex.org/W6770590064","https://openalex.org/W6779269186","https://openalex.org/W6791102956","https://openalex.org/W6795843344","https://openalex.org/W6839029159"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4298221930","https://openalex.org/W2390279801","https://openalex.org/W2777914285","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W3176937389","https://openalex.org/W4408069290"],"abstract_inverted_index":{"Clustered":[0],"federated":[1,13],"learning":[2,14],"(CFL)":[3],"addresses":[4],"the":[5,46,69],"performance":[6],"challenges":[7],"posed":[8],"by":[9,16],"data":[10,22],"heterogeneity":[11],"in":[12,143],"(FL)":[15],"organizing":[17],"edge":[18,72],"devices":[19,73],"with":[20,51,131],"similar":[21],"distributions":[23],"into":[24,106],"clusters,":[25,44],"enabling":[26,127],"collaborative":[27],"model":[28,91,119],"training":[29,111],"tailored":[30],"to":[31,42,56,85],"each":[32,99],"group.":[33],"However,":[34],"existing":[35],"CFL":[36,83],"approaches":[37],"strictly":[38],"limit":[39],"knowledge":[40,50,105],"sharing":[41],"within":[43],"lacking":[45],"integration":[47],"of":[48,71],"global":[49,104],"intra-cluster":[52,107],"training,":[53],"which":[54],"leads":[55],"suboptimal":[57],"performance.":[58,112],"Moreover,":[59],"traditional":[60],"clustering":[61,148],"methods":[62],"incur":[63],"significant":[64],"computational":[65,133,149],"overhead,":[66],"especially":[67],"as":[68],"number":[70],"increases.":[74],"In":[75],"this":[76],"paper,":[77],"we":[78],"propose":[79],"LCFed,":[80],"an":[81],"efficient":[82,118],"framework":[84],"combat":[86],"these":[87],"challenges.":[88],"By":[89],"leveraging":[90],"partitioning":[92],"and":[93,147],"adopting":[94],"distinct":[95],"aggregation":[96],"strategies":[97],"for":[98],"sub-model,":[100],"LCFed":[101,114,139],"effectively":[102],"incorporates":[103],"co-training,":[108],"achieving":[109],"optimal":[110],"Additionally,":[113],"customizes":[115],"a":[116],"computationally":[117],"similarity":[120],"measurement":[121],"method":[122],"based":[123],"on":[124],"low-rank":[125],"models,":[126],"real-time":[128],"cluster":[129],"updates":[130],"minimal":[132],"overhead.":[134],"Extensive":[135],"experiments":[136],"show":[137],"that":[138],"outperforms":[140],"state-of-the-art":[141],"benchmarks":[142],"both":[144],"test":[145],"accuracy":[146],"efficiency.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}