{"id":"https://openalex.org/W4416925253","doi":"https://doi.org/10.1109/wimob66857.2025.11257537","title":"Secure V2P Risk Prediction: A Decentralized Federated Deep Learning Approach","display_name":"Secure V2P Risk Prediction: A Decentralized Federated Deep Learning Approach","publication_year":2025,"publication_date":"2025-10-20","ids":{"openalex":"https://openalex.org/W4416925253","doi":"https://doi.org/10.1109/wimob66857.2025.11257537"},"language":null,"primary_location":{"id":"doi:10.1109/wimob66857.2025.11257537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wimob66857.2025.11257537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)","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/A5118792173","display_name":"Rihab Hmaied","orcid":null},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":true,"raw_author_name":"Rihab Hmaied","raw_affiliation_strings":["University of Carthage Supcom, InnovCom,Tunis,Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Carthage Supcom, InnovCom,Tunis,Tunisia","institution_ids":["https://openalex.org/I179097149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118792174","display_name":"Takoua Kefi","orcid":null},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Takoua Kefi","raw_affiliation_strings":["University of Carthage Supcom, InnovCom,Tunis,Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Carthage Supcom, InnovCom,Tunis,Tunisia","institution_ids":["https://openalex.org/I179097149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038170181","display_name":"Ryma Abassi","orcid":"https://orcid.org/0000-0003-2148-7965"},"institutions":[{"id":"https://openalex.org/I179097149","display_name":"University of Carthage","ror":"https://ror.org/057x6za15","country_code":"TN","type":"education","lineage":["https://openalex.org/I179097149"]}],"countries":["TN"],"is_corresponding":false,"raw_author_name":"Ryma Abassi","raw_affiliation_strings":["University of Carthage Supcom, InnovCom,Tunis,Tunisia"],"affiliations":[{"raw_affiliation_string":"University of Carthage Supcom, InnovCom,Tunis,Tunisia","institution_ids":["https://openalex.org/I179097149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5118792173"],"corresponding_institution_ids":["https://openalex.org/I179097149"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40609593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"61","last_page":"66"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.7398999929428101,"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"}},"topics":[{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.7398999929428101,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.04529999941587448,"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":0.033399999141693115,"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/federated-learning","display_name":"Federated learning","score":0.8471999764442444},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.599399983882904},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.5393999814987183},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.477400004863739},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4537000060081482},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.37610000371932983},{"id":"https://openalex.org/keywords/information-privacy","display_name":"Information privacy","score":0.37310001254081726},{"id":"https://openalex.org/keywords/policy-learning","display_name":"Policy learning","score":0.3515999913215637}],"concepts":[{"id":"https://openalex.org/C2992525071","wikidata":"https://www.wikidata.org/wiki/Q50818671","display_name":"Federated learning","level":2,"score":0.8471999764442444},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7900000214576721},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.599399983882904},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.566100001335144},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5393999814987183},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4537000060081482},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.37610000371932983},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36719998717308044},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.3515999913215637},{"id":"https://openalex.org/C2779582901","wikidata":"https://www.wikidata.org/wiki/Q21013010","display_name":"Distributed learning","level":2,"score":0.34610000252723694},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.33809998631477356},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.28999999165534973},{"id":"https://openalex.org/C101765175","wikidata":"https://www.wikidata.org/wiki/Q577764","display_name":"Communications system","level":2,"score":0.28870001435279846},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.27730000019073486},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C192126672","wikidata":"https://www.wikidata.org/wiki/Q1068715","display_name":"Telecommunications network","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C93996380","wikidata":"https://www.wikidata.org/wiki/Q44127","display_name":"Server","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C205875254","wikidata":"https://www.wikidata.org/wiki/Q17156857","display_name":"Decentralised system","level":3,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wimob66857.2025.11257537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wimob66857.2025.11257537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 21th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)","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":18,"referenced_works":["https://openalex.org/W2167695003","https://openalex.org/W3035574168","https://openalex.org/W4200479733","https://openalex.org/W4206323904","https://openalex.org/W4386231006","https://openalex.org/W4387308345","https://openalex.org/W4390872638","https://openalex.org/W4391770313","https://openalex.org/W4392716614","https://openalex.org/W4394859121","https://openalex.org/W4401214909","https://openalex.org/W4401416537","https://openalex.org/W4402809795","https://openalex.org/W4403407892","https://openalex.org/W4405080229","https://openalex.org/W4406264854","https://openalex.org/W4410500951","https://openalex.org/W4411950044"],"related_works":[],"abstract_inverted_index":{"Federated":[0],"Learning":[1],"(FL)":[2],"is":[3],"increasingly":[4],"adopted":[5],"to":[6],"tackle":[7],"privacy":[8],"concerns":[9],"in":[10],"Intelligent":[11],"Transportation":[12],"Systems":[13],"(ITS),":[14],"particularly":[15],"within":[16,52],"Vehicle-to-Pedestrian":[17],"(V2P)":[18],"communication":[19,72,94],"frameworks.":[20],"Extending":[21],"our":[22],"previous":[23],"research":[24,97],"on":[25],"decentralized":[26,54,105],"federated":[27,106],"learning":[28,33,107],"frameworks":[29],"using":[30,62],"classical":[31],"machine":[32],"algorithms,":[34],"this":[35],"study":[36],"investigates":[37],"the":[38,85,99],"efficacy":[39],"of":[40,103],"advanced":[41],"Convolutional":[42],"Neural":[43],"Networks":[44],"(CNNs),":[45],"specifically":[46],"ResNet18,":[47],"ResNet34,":[48],"MobileNetV2,":[49],"and":[50,71,92,101,114],"EfficientNetB0,":[51],"a":[53,109],"FL":[55],"context.":[56],"We":[57],"comparatively":[58],"evaluate":[59],"these":[60],"models":[61],"performance":[63],"metrics":[64],"such":[65],"as":[66,108],"accuracy,":[67,89],"loss,":[68],"training":[69],"time,":[70],"delay.":[73],"Our":[74],"experimental":[75],"results":[76],"demonstrate":[77],"that":[78],"ResNet34":[79],"achieves":[80],"superior":[81],"overall":[82],"performance,":[83],"offering":[84],"best":[86],"trade-off":[87],"between":[88],"convergence":[90],"efficiency,":[91],"reduced":[93],"overhead.":[95],"This":[96],"confirms":[98],"applicability":[100],"advantages":[102],"CNN-based":[104],"robust":[110],"solution":[111],"for":[112],"secure":[113],"efficient":[115],"V2P":[116],"communication.":[117]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-02T00:00:00"}
