Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network

Basit Öğe Kaydı
dc.authoridBoyaci, Ali/0000-0002-2553-1911
dc.contributor.authorGokcen, Alpaslan
dc.contributor.authorBoyaci, Ali
dc.date.accessioned2024-10-12T19:43:03Z
dc.date.available2024-10-12T19:43:03Z
dc.date.issued2024
dc.departmentİstanbul Ticaret Üniversitesien_US
dc.description.abstractThis study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model's accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.en_US
dc.identifier.doi10.3390/electronics13142820
dc.identifier.issn2079-9292
dc.identifier.issue14en_US
dc.identifier.scopus2-s2.0-85199649144en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/electronics13142820
dc.identifier.urihttps://hdl.handle.net/11467/8714
dc.identifier.volume13en_US
dc.identifier.wosWOS:001277568700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofElectronicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzWoS_2024en_US
dc.subjectdata privacyen_US
dc.subjectreal-time action detectionen_US
dc.subjectintelligent vehiclesen_US
dc.subjectfederated learningen_US
dc.titlePrivacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Networken_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu