Image de couverture de livre

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

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Huang, Yaorong, Luo, Jingtao, Wang, Xuechao
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Distributed, Parallel, and Cluster Computing
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
par: Uddin, Ashab, et autres
Publié: (2025)

Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing
par: Liu, Jiagang, et autres
Publié: (2023)

LLM-Enhanced Deep Reinforcement Learning for Task Offloading in Collaborative Edge Computing
par: Guo, Hao, et autres
Publié: (2026)

A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System
par: Wu, Jianqiu, et autres
Publié: (2023)

Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compression
par: Zhang, Zhenxiao, et autres
Publié: (2024)

Learnable Sparse Customization in Heterogeneous Edge Computing
par: Xue, Jingjing, et autres
Publié: (2024)

DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing
par: Yamansavascilar, Baris, et autres
Publié: (2021)

Decentralized Task Offloading and Load-Balancing for Mobile Edge Computing in Dense Networks
par: Yahya, Mariam, et autres
Publié: (2024)

Model Partition and Resource Allocation for Split Learning in Vehicular Edge Networks
par: Yu, Lu, et autres
Publié: (2024)

FedHFT: Efficient Federated Finetuning with Heterogeneous Edge Clients
par: Ilhan, Fatih, et autres
Publié: (2025)

Non-Federated Multi-Task Split Learning for Heterogeneous Sources
par: Zheng, Yilin, et autres
Publié: (2024)

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks
par: Wang, Shuai, et autres
Publié: (2025)

DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing
par: Gu, Xueying, et autres
Publié: (2024)

AirFed: A Federated Graph-Enhanced Multi-Agent Reinforcement Learning Framework for Multi-UAV Cooperative Mobile Edge Computing
par: Wang, Zhiyu, et autres
Publié: (2025)

Analytic Personalized Federated Meta-Learning
par: Gu, Shunxian, et autres
Publié: (2025)

Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
par: Wu, Jiajun, et autres
Publié: (2023)

Stitching Satellites to the Edge: Pervasive and Efficient Federated LEO Satellite Learning
par: Elmahallawy, Mohamed, et autres
Publié: (2024)

Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
par: Luo, Ping, et autres
Publié: (2026)

Egret: Reinforcement Mechanism for Sequential Computation Offloading in Edge Computing
par: Peng, Haosong, et autres
Publié: (2024)

HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems
par: Lin, Zheng, et autres
Publié: (2025)

Intelligent Task Offloading in VANETs: A Hybrid AI-Driven Approach for Low-Latency and Energy Efficiency
par: Qayyum, Tariq, et autres
Publié: (2025)

Tackling Privacy Heterogeneity in Differentially Private Federated Learning
par: Xu, Ruichen, et autres
Publié: (2026)

PracMHBench: Re-evaluating Model-Heterogeneous Federated Learning Based on Practical Edge Device Constraints
par: Guo, Yuanchun, et autres
Publié: (2025)

Convergence Analysis of Split Federated Learning on Heterogeneous Data
par: Han, Pengchao, et autres
Publié: (2024)

QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing
par: Rahmaty, Iman, et autres
Publié: (2023)

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
par: Zhang, Chen, et autres
Publié: (2025)

STHFL: Spatio-Temporal Heterogeneous Federated Learning
par: Guo, Shunxin, et autres
Publié: (2025)

Federated Model Heterogeneous Matryoshka Representation Learning
par: Yi, Liping, et autres
Publié: (2024)

Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning
par: Wang, Wenhua, et autres
Publié: (2024)

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design
par: Hou, Xiangwang, et autres
Publié: (2025)

Lightweight Federated Learning over Wireless Edge Networks
par: Hou, Xiangwang, et autres
Publié: (2025)

Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings
par: Yuan, Liangqi, et autres
Publié: (2025)

On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments
par: Wang, Leo Muxing, et autres
Publié: (2024)

Knowledge-Driven Federated Graph Learning on Model Heterogeneity
par: Wu, Zhengyu, et autres
Publié: (2025)

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
par: Wang, Xiaolu, et autres
Publié: (2024)

Fed-REACT: Federated Representation Learning for Heterogeneous and Evolving Data
par: Chen, Yiyue, et autres
Publié: (2025)

Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning
par: Wang, Yuqing, et autres
Publié: (2025)

FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
par: Ahmed, Faisal, et autres
Publié: (2024)

Heterogeneous Federated Learning with Prototype Alignment and Upscaling
par: Lee, Gyuejeong, et autres
Publié: (2025)

FLASH: Federated Learning Across Simultaneous Heterogeneities
par: Chang, Xiangyu, et autres
Publié: (2024)