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- W4385486511 abstract "This paper presents the road safety intelligent transport system (RSITS) based on deep federated learning-assisted fog cloud networks. RSITS offers mobile LiDAR sensors and vehicle LiDAR sensors-enabled applications to alert road safety mechanisms. To deal with the complex features of road safety, we trained the large pedestrian and vehicle detection dataset on different road safety fog servers and aggregated them on the centralized cloud. To ensure that constraints such as safety, the accuracy of alarms, response times, security, and deadlines are met, we present a deep federated learning scheduling scheme (DFLSS) that consists of different components: Initially, we bound all applications so that emergency tasks, such as moving an object within 5 m, should be processed locally with the minimum response time. Due to resource constraints and the limitations of devices, other tasks of applications are offloaded to the centralized cloud for processing. To ensure security, each computing node must encrypt and decrypt data before offloading and processing it in DFLSS. Simulation results show that the proposed DFLSS outperformed all existing approaches regarding accuracy, response time, and road safety application deadlines." @default.
- W4385486511 created "2023-08-03" @default.
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- W4385486511 date "2023-01-01" @default.
- W4385486511 modified "2023-09-24" @default.
- W4385486511 title "RSITS: Road Safety Intelligent Transport System in Deep Federated Learning Assisted Fog Cloud Networks" @default.
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- W4385486511 doi "https://doi.org/10.1007/978-3-031-39764-6_2" @default.
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