Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach

2026-05-18

AI Summary

This work addresses the joint task offloading and resource allocation problem for heterogeneous directed acyclic graph (DAG) tasks in vehicular edge computing, while preserving data privacy in distributed training. To this end, the authors propose FedMAGS, a novel framework that integrates graph attention networks (GATs) to model task dependencies, a Seq2Seq policy network to generate structured offloading decisions, and federated meta deep reinforcement learning to enable rapid policy adaptation across edge servers. Experimental results demonstrate that FedMAGS significantly accelerates convergence, reduces task execution latency, enhances system scalability, and lowers communication overhead—all while maintaining data privacy and achieving efficient scheduling of heterogeneous DAG tasks.

Abstract

Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. However, real-world vehicular workloads are typically modeled as heterogeneous directed acyclic graph (DAG) tasks with complex dependency structures, making joint offloading and resource allocation highly challenging. Moreover, distributed MEC deployment raises privacy concerns when collaboratively training learning-based policies. In this paper, we propose a Federated Meta Deep Reinforcement Learning framework with GAT-Seq2Seq modeling (FedMAGS) for heterogeneous task offloading in VEC systems. The proposed approach leverages Graph Attention Networks to capture DAG dependencies, a Seq2Seq-based policy to generate structured offloading decisions, and federated meta-learning to enable fast adaptation across distributed MEC servers without sharing raw data. Extensive simulations demonstrate that FedMAGS achieves faster convergence, lower execution delay, and better scalability compared with state-of-the-art baselines. In addition, the federated design preserves data privacy while reducing communication overhead, making the framework well suited for dynamic and large-scale VEC environments.

Problem

• Vehicular Edge Computing
• Heterogeneous Task Offloading
• Directed Acyclic Graph
• Resource Allocation
• Data Privacy

Innovation

• Federated Meta Learning
• Graph Attention Network
• Task Offloading
• Vehicular Edge Computing
• Seq2Seq Policy