DRL-based federated self-supervised learning for task offloading and resource allocation in ISAC-enabled vehicle edge computing
Xueying Gu, Qiong Wu, Pingyi Fan, Nan Cheng, Wen Chen, Khaled B. Letaief
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 Units (RSUs), ensuring timely services. Our previous work, the FLSimCo algorithm, which uses local resources for federated Self-Supervised Learning (SSL), has a limitation: vehicles often can't complete all iteration tasks. Our improved algorithm offloads partial tasks to RSUs 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 and improves offloading efficiency and accuracy of federated SSL.