Federated Learning Enabled Credit Priority Task Processing for Transportation Big Data

Due to the epidemic COVID-19 spread and Intelligent Transportation System (ITS) development, investigators are now to conduct their research over the generated Transportation Big Data (TBD) in many critical areas, such as medical supplies, food supplies, as well as logistics supplies. At present, Vehicular Edge Computing (VEC) is an emerging paradigm to integrate resources from vehicles, road-site units, base stations, and cloud center to promote the performance of TBD tasks scheduling and running. In this paper, we design a three-layered TBD task processing architecture with a federated learning mechanism for credit priority-based task scheduling and running. In our design, we consider the efficiency of task offloading and misbehavior attack problems simultaneously. We propose a vehicular federated learning framework combined with Multi-Layer Perceptron (MLP) credit measurement, which can preserve the privacy of vehicles and obtain the related features for vehicular credit prediction. We also propose a task offloading algorithm to solve the optimization problem for credit priority task offloading between edge computing servers and vehicles. The proposed solution can prioritize tasks and assign sufficient resources for reliable and active task requesters. Experimental results expose that the proposed mechanism outperforms the state-of-the-art solutions when considering efficiency and attack simultaneously for TBD tasks scheduling and running.