Federated Meta-Learning Based Computation Offloading Approach With Energy-Delay Tradeoffs in UAV-Assisted VEC

Abstract

Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the generalization of models. Therefore, we propose a federated meta-learning (FML) framework to solve computation offloading for UAV-assisted VEC. In this paper, we are concerned with computation offloading of temporary hotspot regions due to traffic congestion. First, we construct a computation offloading problem with energy-delay tradeoffs and convert the problem to a Markov Decision Process (MDP). Then, we use FML to train personalized models for different vehicles while enhancing the generalization, we propose a Graph neural network-based FL Probabilistic Embedding for Actor-critic RL (GFL-PEARL) algorithm. We model the context as a Directed Acyclic Graph (DAG) and use GNN to reconstruct the inference network of the PEARL algorithm to extract the correlation between contexts fully. We dynamically adjust the task priority during the FML training process to improve the sampling efficiency. Finally, we verify the performance of the algorithm through simulation and physical experiments. Experimental results show that our algorithm can reduce average cost and task overtime rate by 31% and 56% respectively compared with the benchmarks.