DRL-Based Federated Self-Supervised Learning for Task Offloading and Resource Allocation in ISAC-Enabled Vehicle Edge Computing

2024-08-27 · Xueying Gu, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief

Intelligent Transportation Systems (ITS) leverage Integrated Sensing and Communications (ISAC) to enhance data exchange between vehicles and infrastructure in the Internet of Vehicles (IoV). This integration inevitably increases computing demands, risking real-time system stability. Vehicle Edge Computing (VEC) addresses this by offloading tasks to Road Side Unit (RSU), ensuring timely services. Our previous work FLSimCo algorithm, which uses local resources for Federated Self-Supervised Learning (SSL), though vehicles often can't complete all iterations task. Our improved algorithm offloads partial task to RSU and optimizes energy consumption by adjusting transmission power, CPU frequency, and task assignment ratios, balancing local and RSU-based training. Meanwhile, setting an offloading threshold further prevents inefficiencies. Simulation results show that the enhanced algorithm reduces energy consumption, improves offloading efficiency and the accuracy of Federated SSL.

📄 PDF Abstract BibTeX arXiv:2408.14831

Code (1)

qiongwu86/federated-ssl-task-offloading-and-resource-allocation 공식 구현

Tasks

CPUEdge-computingISACSelf-Supervised Learning

Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach

2022-06-13 · Lingling Zhang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis 외

The fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs). Due to the limited resource of F-APs, it is imp…

Deep Reinforcement Learning Federated Learning Reinforcement Learning (RL)

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

2026-05-18 · Yaorong Huang, Jingtao Luo, Xuechao Wang arxiv

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 heterogen…

Reinforcement Learning

FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems

2025-06-03 · Frederico Metelo, Alexandre Oliveira, Stevo Racković, Pedro Ákos Costa 외

Edge computing addresses the growing data demands of connected-device networks by placing computational resources closer to end users through decentralized infrastructures. This decentralization challenges traditional, f…

Edge-computing

Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks

2021-04-02 · Sheyda Zarandi, Hina Tabassum

In this paper, we propose a federated deep reinforcement learning framework to solve a multi-objective optimization problem, where we consider minimizing the expected long-term task completion delay and energy consumptio…

Deep Reinforcement Learning Federated Learning Q-Learning reinforcement-learning+2

When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network

2020-09-22 · Shuai Yu, Xu Chen, Zhi Zhou, Xiaowen Gong 외

Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e.g., compu…

Decision Making Deep Reinforcement Learning Edge-computing Federated Learning+1