{"id":"https://openalex.org/W4311922901","doi":"https://doi.org/10.1109/cloudnet55617.2022.9978891","title":"Optimal Network Selection Method Using Federated Learning to Achieve Large-Scale Learning While Preserving Privacy","display_name":"Optimal Network Selection Method Using Federated Learning to Achieve Large-Scale Learning While Preserving Privacy","publication_year":2022,"publication_date":"2022-11-07","ids":{"openalex":"https://openalex.org/W4311922901","doi":"https://doi.org/10.1109/cloudnet55617.2022.9978891"},"language":"en","primary_location":{"id":"doi:10.1109/cloudnet55617.2022.9978891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudnet55617.2022.9978891","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","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/A5012845610","display_name":"Koki Horita","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Koki Horita","raw_affiliation_strings":["Sony Corporation,Tokyo,Japan","Sony Corporation, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Sony Corporation,Tokyo,Japan","institution_ids":[]},{"raw_affiliation_string":"Sony Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072309548","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0002-1658-1079"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Yang","raw_affiliation_strings":["Sony Group Corporation,Tokyo,Japan","Sony Group Corporation, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Sony Group Corporation,Tokyo,Japan","institution_ids":[]},{"raw_affiliation_string":"Sony Group Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057555773","display_name":"Thomas Carette","orcid":null},"institutions":[{"id":"https://openalex.org/I4210112563","display_name":"International Life Sciences Institute Europe","ror":"https://ror.org/01t1g3y18","country_code":"BE","type":"nonprofit","lineage":["https://openalex.org/I4210112563"]}],"countries":["BE"],"is_corresponding":false,"raw_author_name":"Thomas Carette","raw_affiliation_strings":["Sony Europe B.V.,Brussels,Belgium","Sony Europe B.V., Brussels, Belgium"],"affiliations":[{"raw_affiliation_string":"Sony Europe B.V.,Brussels,Belgium","institution_ids":["https://openalex.org/I4210112563"]},{"raw_affiliation_string":"Sony Europe B.V., Brussels, Belgium","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029391924","display_name":"Masanobu Jimbo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masanobu Jimbo","raw_affiliation_strings":["Sony Group Corporation,Tokyo,Japan","Sony Group Corporation, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Sony Group Corporation,Tokyo,Japan","institution_ids":[]},{"raw_affiliation_string":"Sony Group Corporation, Tokyo, Japan","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059219602","display_name":"Akihiro Nakao","orcid":"https://orcid.org/0000-0003-0012-5287"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akihiro Nakao","raw_affiliation_strings":["The University of Tokyo,Tokyo,Japan","The University of Tokyo, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"The University of Tokyo,Tokyo,Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5012845610"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.54460075,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"93","issue":null,"first_page":"220","last_page":"228"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":1.0,"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":1.0,"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/T11598","display_name":"Internet Traffic Analysis and Secure E-voting","score":0.9984999895095825,"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/T10964","display_name":"Wireless Communication Security Techniques","score":0.9958999752998352,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.813601016998291},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6933571696281433},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.667971134185791},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.42946022748947144},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4196704924106598},{"id":"https://openalex.org/keywords/federated-learning","display_name":"Federated learning","score":0.41463473439216614},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4050517976284027},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.3050463795661926}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.813601016998291},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6933571696281433},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.667971134185791},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.42946022748947144},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4196704924106598},{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.41463473439216614},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4050517976284027},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3050463795661926},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cloudnet55617.2022.9978891","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cloudnet55617.2022.9978891","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE 11th International Conference on Cloud Networking (CloudNet)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1991551840","https://openalex.org/W2021432364","https://openalex.org/W2108481447","https://openalex.org/W2110746047","https://openalex.org/W2114089854","https://openalex.org/W2115368602","https://openalex.org/W2133507770","https://openalex.org/W2133795371","https://openalex.org/W2167188015","https://openalex.org/W2184005778","https://openalex.org/W2769147204","https://openalex.org/W3041437500","https://openalex.org/W3123882998","https://openalex.org/W3130016916","https://openalex.org/W3209696639","https://openalex.org/W4283397071","https://openalex.org/W4285876308","https://openalex.org/W4318619660","https://openalex.org/W6676868130","https://openalex.org/W6680005513","https://openalex.org/W6728757088","https://openalex.org/W6770794505","https://openalex.org/W6779269186","https://openalex.org/W6791146863","https://openalex.org/W6838886547"],"related_works":["https://openalex.org/W4298221930","https://openalex.org/W2777914285","https://openalex.org/W3013363440","https://openalex.org/W4287823391","https://openalex.org/W4312762663","https://openalex.org/W4317941881","https://openalex.org/W4229067761","https://openalex.org/W4308527955","https://openalex.org/W4366829214","https://openalex.org/W4225482289"],"abstract_inverted_index":{"These":[0],"days,":[1],"smartphones":[2,57,277],"are":[3,94],"equipped":[4],"with":[5,19,30,150,204,220,223,278],"multiple":[6],"radio":[7],"access":[8,218],"technologies":[9],"(RAT)":[10],"such":[11,123],"as":[12,124],"wireless":[13],"LAN":[14],"(WLAN)":[15],"and":[16,82,128,162,174,208,233,268],"4G/5G":[17],"cellular":[18],"autonomous":[20],"switching":[21,32,80],"between":[22],"them.":[23],"However,":[24],"it":[25,72,129],"is":[26,73,103,116,130,147],"often":[27],"the":[28,50,84,88,137,151,166,215,235,244,248,259],"case":[29],"RAT":[31],"algorithms":[33,146],"being":[34],"based":[35,86],"on":[36,40,49,87,107,160,201,237,276],"availability":[37],"but":[38],"not":[39,148],"predicted":[41],"quality":[42,78,216],"of":[43,52,110,154,168,217],"communications,":[44],"which":[45],"poses":[46],"significant":[47],"frustrations":[48],"part":[51],"users,":[53,71],"especially":[54],"when":[55],"their":[56],"connect":[58],"to":[59,65,69,75,105,132,158,178,254,265],"poor-quality":[60],"WLAN":[61,77,118],"automatically.":[62],"In":[63,140],"order":[64],"provide":[66],"uninterrupted":[67],"communication":[68,251],"smartphone":[70,156,203],"necessary":[74],"predict":[76],"before":[79],"networks":[81],"change":[83],"network":[85,98],"result.":[89],"Although":[90],"machine":[91,101,144],"learning":[92,102,145],"approaches":[93],"useful":[95],"for":[96],"predicting":[97],"quality,":[99],"conventional":[100],"infeasible":[104],"train":[106],"large":[108],"amounts":[109],"data":[111,119],"collected":[112],"from":[113,136],"users.":[114],"It":[115,213],"because":[117],"contain":[120],"private":[121,211],"information":[122],"user":[125,134],"location":[126],"information,":[127],"possible":[131,149],"track":[133],"behavior":[135],"training":[138,161,200,270],"data.":[139],"addition,":[141],"employing":[142],"complex":[143],"computational":[152,280],"resources":[153],"a":[155,176,202],"due":[157],"constraints":[159],"inference":[163],"time.":[164],"To":[165,181],"best":[167],"our":[169],"knowledge,":[170],"no":[171,266],"paper":[172,186],"implements":[173],"evaluates":[175],"solution":[177],"this":[179,183,185],"problem.":[180],"solve":[182],"issue,":[184],"utilizes":[187],"latent":[188],"class":[189],"regression":[190],"model":[191],"trained":[192],"by":[193,231],"Federated":[194],"Learning.":[195],"Our":[196],"proposal":[197],"achieves":[198],"on-device":[199],"limited":[205,279],"computer":[206],"resource":[207],"protects":[209],"users\u2019":[210],"information.":[212],"predicts":[214],"points":[219],"various":[221],"characteristics":[222],"high":[224],"accuracy":[225],"(95.0%":[226],"precision,":[227],"23.7%":[228],"recall).":[229],"Moreover,":[230],"developing":[232],"installing":[234],"application":[236,250],"an":[238],"Android":[239],"smartphone,":[240],"we":[241],"show":[242],"that":[243],"proposed":[245],"method":[246],"reduces":[247],"actual":[249],"disruption":[252],"time":[253],"within":[255,271],"3":[256],"seconds":[257],"at":[258],"75th":[260],"percentile":[261],"(85%":[262],"reduction":[263],"compared":[264],"functionality)":[267],"completes":[269],"1":[272],"second":[273],"per":[274],"session":[275],"resources.":[281]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
