FedDOVe: A Federated Deep Q-learning-based Offloading for Vehicular fog computing

Connected Autonomous Vehicles (CAVs) aim to provide various smart transportation applications which have computation-intensive tasks. The vehicles having less availability of computational resources offload tasks to roadside units (RSUs) for processing. Renewable energy-powered RSUs have limited energy, storage, and computational capabilities. To reduce the computation load at RSUs, vehicular fog computing is used for computation-intensive tasks. Vehicular fog computing brings the computational resources nearer to the requesting devices for reducing the latency in the request fulfillment. RSU computes the tasks either locally or offloads computation-intensive tasks to the fog server for further processing. In vehicular fog computing, one of the most important problems is to find an optimal association of RSUs with fog servers for computation offloading. A significant energy consumption occurs across RSUs in offloading computation-intensive tasks to fog servers. On the other hand, fog servers may have uneven computation load due to varying offloading rate across the road segments. The non-uniform distribution of computation load across fog servers may increase the latency in the request fulfillment. In this paper, we propose an online erated eep Q-learning-based ffloading technique for hicular fog computing (FedDOVe) which jointly optimizes the energy consumption across RSUs and load balancing across fog servers. FedDOVe is a model-free reinforcement learning approach that uses the global information for finding an optimal association between RSUs and fog servers. Simulation results show that the proposed FedDOVe reduces the energy consumption by about 47%–49% and improves load balancing by about 60%–65% as compared to other existing offloading techniques.