Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics

Intelligent logistics empowered by artificial intelligence (AI) has become an inevitable trend in the development of modern logistics, thus, a convenient and efficient logistics system has attracted widespread attention. However, how to use AI to execute computation-intensive applications on resource-constrained logistics vehicles (LVs) still faces enormous challenges. For the dependent applications in intelligent logistics, this article investigates a task dynamic offloading strategy for LVs-edge collaboration with multiple dependent tasks, considering the intertask dependency, to guarantee the Quality-of-Service (QoS) requirements of LVs. First, the dependent application ARCore is modeled and transformed into a model with a linear execution sequence. Then, based on this task model, the joint task offloading and resource allocation problem is formulated. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the delay tolerance and computing resource constraints of the tasks. Furthermore, we propose a federated LVs-edge collaborative computation (FECC) offloading framework to solve the optimization problem, which only requires each agent to share its model parameters without sharing local training data, thereby reducing the computation complexity and signaling overhead of the multiagent training process. Numerical results show that the proposed strategy has significant advantages in terms of total system cost compared to the baseline strategy.