{"id":"https://openalex.org/W4409659397","doi":"https://doi.org/10.1109/tce.2025.3563240","title":"A Two-Phase Client Selection Strategy for Cost-Optimal Federated Learning in Traffic Flow Prediction","display_name":"A Two-Phase Client Selection Strategy for Cost-Optimal Federated Learning in Traffic Flow Prediction","publication_year":2025,"publication_date":"2025-04-22","ids":{"openalex":"https://openalex.org/W4409659397","doi":"https://doi.org/10.1109/tce.2025.3563240"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2025.3563240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3563240","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","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/A5101643168","display_name":"Weiwen Zhang","orcid":"https://orcid.org/0000-0002-5098-6459"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwen Zhang","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-5098-6459","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101601666","display_name":"Shuo Yang","orcid":"https://orcid.org/0009-0006-6220-8490"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuo Yang","raw_affiliation_strings":["School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003608577","display_name":"Yifeng Jiang","orcid":"https://orcid.org/0000-0001-8739-1697"},"institutions":[{"id":"https://openalex.org/I49835588","display_name":"Macao Polytechnic University","ror":"https://ror.org/02sf5td35","country_code":"MO","type":"education","lineage":["https://openalex.org/I49835588"]}],"countries":["MO"],"is_corresponding":false,"raw_author_name":"Yifeng Jiang","raw_affiliation_strings":["Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","Faculty of Applied Sciences, Macao Polytechnic University, Macao, China"],"raw_orcid":"https://orcid.org/0000-0001-8739-1697","affiliations":[{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macau, China","institution_ids":["https://openalex.org/I49835588"]},{"raw_affiliation_string":"Faculty of Applied Sciences, Macao Polytechnic University, Macao, China","institution_ids":["https://openalex.org/I49835588"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.8445,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.83244588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"71","issue":"2","first_page":"2955","last_page":"2964"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9966999888420105,"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":0.9966999888420105,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.9751999974250793,"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":0.9670000076293945,"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":0.6880128979682922},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.6023201942443848},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3936167359352112},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3717718720436096},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.328677773475647},{"id":"https://openalex.org/keywords/distributed-computing","display_name":"Distributed computing","score":0.3256979286670685}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6880128979682922},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6023201942443848},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3936167359352112},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3717718720436096},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.328677773475647},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.3256979286670685}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2025.3563240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2025.3563240","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3246172301","display_name":null,"funder_award_id":"2024A1515011885","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G3366847058","display_name":null,"funder_award_id":"2023A1515011577","funder_id":"https://openalex.org/F4320337111","funder_display_name":"Basic and Applied Basic Research Foundation of Guangdong Province"},{"id":"https://openalex.org/G7671022659","display_name":null,"funder_award_id":"62002071","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7780485600","display_name":null,"funder_award_id":"2023A1515011577","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"},{"id":"https://openalex.org/G8805651664","display_name":null,"funder_award_id":"2024A1515011885","funder_id":"https://openalex.org/F4320320671","funder_display_name":"National Research Foundation"}],"funders":[{"id":"https://openalex.org/F4320320671","display_name":"National Research Foundation","ror":"https://ror.org/05s0g1g46"},{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2011925575","https://openalex.org/W2023835067","https://openalex.org/W2116175219","https://openalex.org/W2165232124","https://openalex.org/W2397723600","https://openalex.org/W2798720628","https://openalex.org/W2920095265","https://openalex.org/W3010852232","https://openalex.org/W3093755649","https://openalex.org/W3109847748","https://openalex.org/W3112885687","https://openalex.org/W3128382687","https://openalex.org/W3163105316","https://openalex.org/W4205256428","https://openalex.org/W4226524907","https://openalex.org/W4293732344","https://openalex.org/W4302275297","https://openalex.org/W4321609156","https://openalex.org/W4323519261","https://openalex.org/W4385756484","https://openalex.org/W4385893206","https://openalex.org/W4386609221","https://openalex.org/W4387145983","https://openalex.org/W4387789936","https://openalex.org/W4388040473","https://openalex.org/W4389428616","https://openalex.org/W4389879917","https://openalex.org/W4390187498","https://openalex.org/W4390603935","https://openalex.org/W4391092984","https://openalex.org/W4391326609","https://openalex.org/W4393171375","https://openalex.org/W4398765803","https://openalex.org/W4400879910","https://openalex.org/W4402775751","https://openalex.org/W4404056768","https://openalex.org/W4404563667"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4387369504","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W3107602296","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"Federated":[0],"learning":[1,28,59],"has":[2],"shown":[3],"its":[4],"great":[5],"applicability":[6],"in":[7,42,61],"intelligent":[8],"transportation":[9,45],"systems,":[10],"where":[11],"prediction":[12],"models":[13],"can":[14],"be":[15],"trained":[16],"across":[17],"regions":[18],"or":[19],"cities":[20],"without":[21],"leaking":[22],"raw":[23],"data.":[24],"However,":[25],"current":[26],"federated":[27,58],"approaches":[29],"often":[30],"ignore":[31],"energy":[32,35,70,118],"consumption,":[33],"while":[34,76],"consumption":[36,71,119],"is":[37,114],"playing":[38],"a":[39,52],"pivotal":[40],"role":[41],"sustainability":[43],"of":[44,72,117,136,148,155],"systems.":[46],"In":[47],"this":[48],"paper,":[49],"we":[50,87,127],"propose":[51],"Two-Phase":[53],"Client":[54],"Selection":[55],"strategy":[56],"for":[57,74],"(FedTPCS)":[60],"traffic":[62],"flow":[63],"prediction,":[64],"aiming":[65],"to":[66,83,91,123,140,165,178],"minimize":[67],"the":[68,105,109,115,142,153,156,171,184,193],"total":[69],"clients":[73,93,144],"participation":[75],"considering":[77],"device":[78,85],"and":[79,99,120,168,181,187,190],"data":[80,125],"heterogeneity.":[81],"First,":[82],"tackle":[84,124],"heterogeneity,":[86,126],"leverage":[88,128],"K-means":[89],"clustering":[90,131],"group":[92,107],"based":[94,132],"on":[95,133,160],"their":[96],"computing":[97],"power":[98],"geographic":[100],"distance.":[101],"We":[102,151],"strategically":[103],"select":[104],"clustered":[106],"with":[108],"lowest":[110],"average":[111],"cost":[112,175],"that":[113],"combination":[116],"latency.":[121],"Second,":[122],"affinity":[129],"propagation":[130],"cosine":[134],"similarity":[135],"model":[137],"update":[138],"vectors":[139],"divide":[141],"selected":[143],"into":[145],"several":[146],"subgroups":[147],"similar":[149],"clients.":[150],"evaluate":[152],"performance":[154],"proposed":[157],"FedTPCS":[158,172],"algorithm":[159,173],"two":[161],"public":[162],"datasets.":[163],"Compared":[164],"FedAvg,":[166],"FedAEB":[167],"Greedy":[169],"algorithms,":[170],"reduces":[174],"by":[176],"up":[177],"56%,":[179],"30%,":[180],"20%":[182],"under":[183,192],"PeMS":[185],"dataset,":[186,196],"50%,":[188],"28%,":[189],"18%":[191],"Highways":[194],"England":[195],"respectively.":[197]},"counts_by_year":[{"year":2026,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}