IoV environment exploring coordination: A federated learning approach

Abstract

Exploring open fields with coordinated unmanned vehicles is popular in academia and industries. One of the most impressive applicable approaches is the Internet of Vehicles (IoV). The IoV connects vehicles, road infrastructures, and communication facilities to provide solutions for exploration tasks. However, the coordination of acquiring information from multi-vehicles may risk data privacy. To this end, sharing high-quality experiences instead of raw data has become an urgent demand. This paper employs a Deep Reinforcement Learning (DRL) method to enable IoVs to generate training data with prioritized experience and states, which can support the IoV to explore the environment more efficiently. Moreover, a Federated Learning (FL) experience sharing model is established to guarantee the vehicles' privacy. The numerical results show that the proposed method presents a better successful sharing rate and a more stable convergence within the comparison of fundamental methods. The experiments also suggest that the proposed method could support agents without full information to achieve the tasks.