{"id":"https://openalex.org/W4385488444","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191762","title":"Visually Analysing the Fairness of Clustered Federated Learning with Non-IID Data","display_name":"Visually Analysing the Fairness of Clustered Federated Learning with Non-IID Data","publication_year":2023,"publication_date":"2023-06-18","ids":{"openalex":"https://openalex.org/W4385488444","doi":"https://doi.org/10.1109/ijcnn54540.2023.10191762"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn54540.2023.10191762","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191762","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","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/A5100744865","display_name":"Huang Li","orcid":"https://orcid.org/0000-0003-4669-3466"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Huang","raw_affiliation_strings":["Tsinghua University,Beijing,China","Tsinghua University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100653962","display_name":"Weiwei Cui","orcid":"https://orcid.org/0000-0003-0870-7628"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwei Cui","raw_affiliation_strings":["Microsoft Research Asia,Beijing,China","Microsoft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia,Beijing,China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081758219","display_name":"Bin Zhu","orcid":"https://orcid.org/0000-0002-0716-6105"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhu","raw_affiliation_strings":["Microsoft Research Asia,Beijing,China","Microsoft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia,Beijing,China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101789836","display_name":"Haidong Zhang","orcid":"https://orcid.org/0000-0001-7411-8042"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haidong Zhang","raw_affiliation_strings":["Microsoft Research Asia,Beijing,China","Microsoft Research Asia, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia,Beijing,China","institution_ids":["https://openalex.org/I4210113369"]},{"raw_affiliation_string":"Microsoft Research Asia, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9998000264167786,"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":0.9998000264167786,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9668999910354614,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11045","display_name":"Privacy, Security, and Data Protection","score":0.9280999898910522,"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":0.8013343811035156},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.6822490096092224},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6144269704818726},{"id":"https://openalex.org/keywords/independent-and-identically-distributed-random-variables","display_name":"Independent and identically distributed random variables","score":0.5339241027832031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5170322060585022},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.47922283411026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47918736934661865},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.4437882602214813},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.42650121450424194},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.37181150913238525},{"id":"https://openalex.org/keywords/random-variable","display_name":"Random variable","score":0.17842906713485718},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09852060675621033}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8013343811035156},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.6822490096092224},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6144269704818726},{"id":"https://openalex.org/C141513077","wikidata":"https://www.wikidata.org/wiki/Q378542","display_name":"Independent and identically distributed random variables","level":3,"score":0.5339241027832031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5170322060585022},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.47922283411026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47918736934661865},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.4437882602214813},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.42650121450424194},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37181150913238525},{"id":"https://openalex.org/C122123141","wikidata":"https://www.wikidata.org/wiki/Q176623","display_name":"Random variable","level":2,"score":0.17842906713485718},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09852060675621033},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn54540.2023.10191762","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ijcnn54540.2023.10191762","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.49000000953674316,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":67,"referenced_works":["https://openalex.org/W2040975718","https://openalex.org/W2067752346","https://openalex.org/W2073459066","https://openalex.org/W2166944917","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2250539671","https://openalex.org/W2734358244","https://openalex.org/W2805695370","https://openalex.org/W2807006176","https://openalex.org/W2912213068","https://openalex.org/W2912592113","https://openalex.org/W2913155064","https://openalex.org/W2946193510","https://openalex.org/W2966683668","https://openalex.org/W2977072935","https://openalex.org/W2995022099","https://openalex.org/W3006555759","https://openalex.org/W3019945581","https://openalex.org/W3021654819","https://openalex.org/W3043758338","https://openalex.org/W3080934299","https://openalex.org/W3089578458","https://openalex.org/W3091635927","https://openalex.org/W3100779497","https://openalex.org/W3118608800","https://openalex.org/W3129362180","https://openalex.org/W3133814152","https://openalex.org/W3138531978","https://openalex.org/W3156220053","https://openalex.org/W3181414820","https://openalex.org/W3196371845","https://openalex.org/W3196691156","https://openalex.org/W3198906045","https://openalex.org/W3206887799","https://openalex.org/W4200580682","https://openalex.org/W4200631596","https://openalex.org/W4229029907","https://openalex.org/W4235152108","https://openalex.org/W4281753687","https://openalex.org/W4285722492","https://openalex.org/W4285876308","https://openalex.org/W4286912360","https://openalex.org/W4287332481","https://openalex.org/W4289147229","https://openalex.org/W4298326929","https://openalex.org/W4302275297","https://openalex.org/W4307362130","https://openalex.org/W4309432326","https://openalex.org/W4318619660","https://openalex.org/W6663928093","https://openalex.org/W6668990524","https://openalex.org/W6728757088","https://openalex.org/W6752029299","https://openalex.org/W6756756286","https://openalex.org/W6758757267","https://openalex.org/W6758782652","https://openalex.org/W6762879059","https://openalex.org/W6773817997","https://openalex.org/W6779269186","https://openalex.org/W6787972765","https://openalex.org/W6791444617","https://openalex.org/W6794664019","https://openalex.org/W6802161216","https://openalex.org/W6802646539","https://openalex.org/W6841271449","https://openalex.org/W6846585165"],"related_works":["https://openalex.org/W2386430105","https://openalex.org/W2356521405","https://openalex.org/W2038534795","https://openalex.org/W2384358604","https://openalex.org/W1567829292","https://openalex.org/W3001063351","https://openalex.org/W3196905815","https://openalex.org/W2351370765","https://openalex.org/W3133811809","https://openalex.org/W1967452726"],"abstract_inverted_index":{"As":[0],"a":[1,12,123,196,209,219,225],"popular":[2],"distributed":[3,34],"privacy-preserving":[4],"machine":[5],"learning":[6,9,139,191],"approach,":[7],"federated":[8,227],"(FL)":[10],"trains":[11],"global":[13,100,112],"model":[14,101],"across":[15,67],"numerous":[16],"clients":[17,50],"without":[18],"directly":[19],"sharing":[20],"their":[21],"private":[22],"training":[23],"data.":[24],"A":[25,141],"fundamental":[26],"challenge":[27],"of":[28,74,79,98,110,134,145,172],"FL":[29,43,96],"is":[30,152,195,206],"non-independently":[31],"and":[32,102,131,185,212,224],"identically":[33],"(non-IID)":[35],"data,":[36],"which":[37],"can":[38,177],"be":[39,178],"addressed":[40],"by":[41,162],"clustered":[42],"(CFL)":[44],"that":[45,76],"explores":[46],"inherent":[47],"partitions":[48],"among":[49],"to":[51,62,106,129,154,168,180,186,200],"capture":[52],"heterogeneous":[53],"local":[54],"data":[55],"distributions.":[56],"Existing":[57],"CFL":[58,108,138,202,222],"methods,":[59],"however,":[60],"tend":[61],"optimise":[63],"the":[64,71,94,107,117,137,149],"mean":[65,150],"accuracy":[66],"all":[68],"clients,":[69],"overlooking":[70],"important":[72],"issue":[73],"fairness,":[75],"is,":[77],"uniformity":[78],"clients'":[80],"performance.":[81],"Besides,":[82],"prior":[83],"FL-":[84],"related":[85],"visual":[86],"analytics":[87],"(VA)":[88],"frameworks":[89],"are":[90,103,160],"developed":[91],"based":[92],"on":[93],"standard":[95],"scenario":[97],"single":[99],"hence":[104],"inapplicable":[105],"case":[109,214],"multiple":[111],"(cluster)":[113],"models.":[114],"To":[115],"fill":[116],"research":[118],"gap,":[119],"this":[120],"study":[121],"proposes":[122],"novel":[124],"VA":[125],"framework":[126,198],"named":[127],"cCFLvis":[128],"illustrate":[130],"analyse":[132],"variation":[133,184],"fairness":[135,183],"during":[136],"process.":[140],"new":[142],"metric,":[143],"change":[144],"absolute":[146],"deviation":[147],"from":[148],"(\u0394AD),":[151],"introduced":[153],"quantify":[155],"such":[156],"variation.":[157],"Client":[158],"embeddings":[159],"generated":[161],"contrastive":[163],"learning-based":[164],"dimensionality":[165],"reduction":[166],"techniques":[167],"facilitate":[169],"2-d":[170],"visualisation":[171],"client":[173],"disparity.":[174],"Visual":[175],"cues":[176],"drawn":[179],"help":[181],"understand":[182],"offer":[187],"clues":[188],"for":[189],"improving":[190],"outcomes.":[192],"cCFL":[193],"vis":[194],"general":[197],"applicable":[199],"various":[201],"methods.":[203],"Its":[204],"effectiveness":[205],"showcased":[207],"in":[208],"simple":[210],"demonstration":[211],"two":[213],"studies,":[215],"each":[216],"conducted":[217],"with":[218],"distinct":[220],"representative":[221],"method":[223],"different":[226],"non-IID":[228],"dataset.":[229]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}