{"id":"https://openalex.org/W4391936064","doi":"https://doi.org/10.1109/tits.2024.3362058","title":"Adaptive Segmentation Enhanced Asynchronous Federated Learning for Sustainable Intelligent Transportation Systems","display_name":"Adaptive Segmentation Enhanced Asynchronous Federated Learning for Sustainable Intelligent Transportation Systems","publication_year":2024,"publication_date":"2024-02-19","ids":{"openalex":"https://openalex.org/W4391936064","doi":"https://doi.org/10.1109/tits.2024.3362058"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2024.3362058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3362058","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5055675863","display_name":"Xiaokang Zhou","orcid":"https://orcid.org/0000-0003-3488-4679"},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]},{"id":"https://openalex.org/I4210126580","display_name":"RIKEN Center for Advanced Intelligence Project","ror":"https://ror.org/03ckxwf91","country_code":"JP","type":"facility","lineage":["https://openalex.org/I4210110652","https://openalex.org/I4210126580"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiaokang Zhou","raw_affiliation_strings":["Faculty of Data Science, Shiga University, Hikone, Japan","RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3488-4679","affiliations":[{"raw_affiliation_string":"Faculty of Data Science, Shiga University, Hikone, Japan","institution_ids":["https://openalex.org/I171494771"]},{"raw_affiliation_string":"RIKEN Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan","institution_ids":["https://openalex.org/I4210126580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073441538","display_name":"Wei Liang","orcid":"https://orcid.org/0000-0002-0689-256X"},"institutions":[{"id":"https://openalex.org/I4210114190","display_name":"Shanghai Zhangjiang Laboratory","ror":"https://ror.org/0208qbg77","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114190"]},{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liang","raw_affiliation_strings":["School of Advanced Interdisciplinary Studies and School of Management Science and Engineering, Hunan University of Technology and Business, Changsha, China","Xiangjiang Laboratory, Changsha, China"],"raw_orcid":"https://orcid.org/0000-0002-0689-256X","affiliations":[{"raw_affiliation_string":"School of Advanced Interdisciplinary Studies and School of Management Science and Engineering, Hunan University of Technology and Business, Changsha, China","institution_ids":["https://openalex.org/I49934816"]},{"raw_affiliation_string":"Xiangjiang Laboratory, Changsha, China","institution_ids":["https://openalex.org/I4210114190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036808755","display_name":"Akira Kawai","orcid":"https://orcid.org/0000-0003-2116-586X"},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akira Kawai","raw_affiliation_strings":["Faculty of Data Science, Shiga University, Hikone, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Data Science, Shiga University, Hikone, Japan","institution_ids":["https://openalex.org/I171494771"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037408785","display_name":"Kaoru Fueda","orcid":null},"institutions":[{"id":"https://openalex.org/I171494771","display_name":"Shiga University","ror":"https://ror.org/01vvhy971","country_code":"JP","type":"education","lineage":["https://openalex.org/I171494771"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kaoru Fueda","raw_affiliation_strings":["Faculty of Data Science, Shiga University, Hikone, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Data Science, Shiga University, Hikone, Japan","institution_ids":["https://openalex.org/I171494771"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101916568","display_name":"Jinhua She","orcid":"https://orcid.org/0000-0003-3165-5045"},"institutions":[{"id":"https://openalex.org/I148798404","display_name":"Tokyo University of Technology","ror":"https://ror.org/021a26605","country_code":"JP","type":"education","lineage":["https://openalex.org/I148798404"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jinhua She","raw_affiliation_strings":["School of Engineering, Tokyo University of Technology, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0000-0003-3165-5045","affiliations":[{"raw_affiliation_string":"School of Engineering, Tokyo University of Technology, Tokyo, Japan","institution_ids":["https://openalex.org/I148798404"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091532881","display_name":"Kevin I\u2010Kai Wang","orcid":"https://orcid.org/0000-0001-8450-2558"},"institutions":[{"id":"https://openalex.org/I154130895","display_name":"University of Auckland","ror":"https://ror.org/03b94tp07","country_code":"NZ","type":"education","lineage":["https://openalex.org/I154130895"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Kevin I-Kai Wang","raw_affiliation_strings":["Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland, New Zealand"],"raw_orcid":"https://orcid.org/0000-0001-8450-2558","affiliations":[{"raw_affiliation_string":"Department of Electrical, Computer, and Software Engineering, University of Auckland, Auckland, New Zealand","institution_ids":["https://openalex.org/I154130895"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":16.8663,"has_fulltext":false,"cited_by_count":94,"citation_normalized_percentile":{"value":0.998043,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":"25","issue":"7","first_page":"6658","last_page":"6666"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8978999853134155,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.8978999853134155,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.8295000195503235,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.8203999996185303,"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/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.7173693180084229},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.6773079037666321},{"id":"https://openalex.org/keywords/asynchronous-learning","display_name":"Asynchronous learning","score":0.6757643818855286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6334206461906433},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45876142382621765},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.3558087944984436},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3509836196899414},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34042567014694214},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.3160226345062256},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.23016607761383057},{"id":"https://openalex.org/keywords/synchronous-learning","display_name":"Synchronous learning","score":0.089989572763443}],"concepts":[{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.7173693180084229},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.6773079037666321},{"id":"https://openalex.org/C2777072894","wikidata":"https://www.wikidata.org/wiki/Q4812204","display_name":"Asynchronous learning","level":5,"score":0.6757643818855286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6334206461906433},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45876142382621765},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.3558087944984436},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3509836196899414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34042567014694214},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.3160226345062256},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.23016607761383057},{"id":"https://openalex.org/C19122763","wikidata":"https://www.wikidata.org/wiki/Q7662215","display_name":"Synchronous learning","level":4,"score":0.089989572763443},{"id":"https://openalex.org/C88610354","wikidata":"https://www.wikidata.org/wiki/Q1813494","display_name":"Teaching method","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C51672120","wikidata":"https://www.wikidata.org/wiki/Q303446","display_name":"Cooperative learning","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2024.3362058","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2024.3362058","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5699999928474426,"display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1864093853","display_name":"Research on Causality-Inspired Deep Learning Model for Anomaly Detection with Imbalanced Data","funder_award_id":"23K11064","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G2071613473","display_name":"\u591a\u57df\u5065\u5eb7\u5927\u6570\u636e\u9a71\u52a8\u7684\u91cd\u5927\u4f20\u67d3\u75c5\u75ab\u60c5\u65e9\u671f\u8bc6\u522b\u65b9\u6cd5\u7814\u7a76","funder_award_id":"62072171","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G220146140","display_name":null,"funder_award_id":"72091515","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W2521054263","https://openalex.org/W2564946422","https://openalex.org/W2914434280","https://openalex.org/W2962814013","https://openalex.org/W2963318081","https://openalex.org/W2969468102","https://openalex.org/W2969648982","https://openalex.org/W2971544482","https://openalex.org/W2977797911","https://openalex.org/W2989289980","https://openalex.org/W2998045710","https://openalex.org/W2999469653","https://openalex.org/W3000340284","https://openalex.org/W3026202797","https://openalex.org/W3034881756","https://openalex.org/W3042070225","https://openalex.org/W3042621011","https://openalex.org/W3043527956","https://openalex.org/W3046465350","https://openalex.org/W3085873903","https://openalex.org/W3094736543","https://openalex.org/W3098486933","https://openalex.org/W3105886168","https://openalex.org/W3119924535","https://openalex.org/W3161976591","https://openalex.org/W3169736721","https://openalex.org/W4225662012","https://openalex.org/W4366250585"],"related_works":["https://openalex.org/W4226047841","https://openalex.org/W2156559761","https://openalex.org/W3020208395","https://openalex.org/W2496167052","https://openalex.org/W2041343407","https://openalex.org/W2058515809","https://openalex.org/W4295242624","https://openalex.org/W2024923645","https://openalex.org/W2725154194","https://openalex.org/W2097058819"],"abstract_inverted_index":{"The":[0,18],"proliferation":[1],"of":[2,12,21,44,162,202],"advanced":[3],"embedded":[4],"and":[5,24,34,38,42,65,81,88,112,187,199],"communication":[6,200],"technologies":[7],"has":[8],"facilitated":[9],"the":[10,40,109,133,156,167,174,181,196],"possibility":[11],"modern":[13],"Intelligent":[14],"Transportation":[15],"System":[16],"(ITS).":[17],"hierarchical":[19],"nature":[20],"such":[22],"large-scale":[23,80],"distributed":[25,55,214],"systems":[26,83],"brings":[27],"obvious":[28],"challenges":[29],"in":[30,114,212],"creating":[31],"a":[32,118,122,160,213],"scalable":[33],"sustainable":[35,115],"computing":[36],"environment,":[37],"hence":[39],"development":[41],"application":[43,216],"edge":[45,140],"intelligence":[46],"become":[47],"critical.":[48],"Federated":[49,102],"learning":[50,57,67,110,198],"(FL),":[51],"as":[52],"an":[53,97,192],"emerging":[54],"machine":[56],"paradigm,":[58],"aims":[59],"to":[60,107,130,143,154],"offer":[61],"secure":[62],"knowledge":[63],"sharing":[64],"effective":[66],"across":[68,177],"multiple":[69,139],"devices.":[70],"However,":[71],"conventional":[72,209],"FL":[73,158,176,210],"may":[74],"fall":[75],"into":[76,138],"trouble":[77],"when":[78],"facing":[79],"network-agnostic":[82],"with":[84,207],"fast":[85],"moving":[86],"devices":[87],"changing":[89],"network":[90],"attributes.":[91,146],"In":[92],"this":[93],"study,":[94],"we":[95],"propose":[96],"Adaptive":[98],"Segmentation":[99],"enhanced":[100],"Asynchronous":[101],"Learning":[103],"(AS-AFL)":[104],"model,":[105,205],"aiming":[106],"improve":[108],"efficiency":[111],"reliability":[113],"ITS":[116,215],"via":[117,166,180],"decentralized":[119],"fashion.":[120],"Specifically,":[121],"meta-learning":[123],"based":[124,190],"adaptive":[125],"segmentation":[126],"scheme":[127],"is":[128,151],"designed":[129],"automatically":[131],"separate":[132],"client":[134,164],"nodes":[135,165],"(e.g.,":[136],"vehicles)":[137],"groups":[141,179],"according":[142],"their":[144],"homogeneous":[145],"An":[147],"integrated":[148],"aggregation":[149],"mechanism":[150],"then":[152],"developed":[153],"realize":[155],"horizontal":[157],"among":[159],"group":[161],"similar":[163],"so-called":[168,182],"intra-group":[169],"synchronous":[170],"aggregation,":[171],"while":[172],"allowing":[173],"vertical":[175],"different":[178],"inter-group":[183],"asynchronous":[184],"aggregation.":[185],"Experiment":[186],"evaluation":[188],"results":[189],"on":[191],"open-source":[193],"dataset":[194],"demonstrate":[195],"outstanding":[197],"performance":[201],"our":[203],"proposed":[204],"compared":[206],"several":[208],"schemes":[211],"scenario.":[217]},"counts_by_year":[{"year":2026,"cited_by_count":13},{"year":2025,"cited_by_count":51},{"year":2024,"cited_by_count":30}],"updated_date":"2026-07-25T09:21:30.201066","created_date":"2025-10-10T00:00:00"}