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

TL;DR

This paper proposes an improved federated self-supervised learning algorithm for vehicle edge computing that offloads tasks to RSUs, optimizing energy use and enhancing system efficiency in ISAC-enabled intelligent transportation systems.

Contribution

It introduces a novel offloading and resource allocation strategy that balances local and RSU training, reducing energy consumption and improving SSL accuracy in vehicle edge computing.

Findings

Reduced energy consumption in vehicle edge computing

Improved offloading efficiency and task completion rates

Enhanced accuracy of federated self-supervised learning

Abstract

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.