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

arXiv: https://arxiv.org/abs/2408.14831 · HTML: https://mlsanity.com/paper/2408.14831/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 1 of 1)

- [Mobility-Aware Federated Self-supervised Learning in Vehicular Network](https://mlsanity.com/paper/2408.00256/) (2024)

## Built on by (most cited first, 7 of 7)

- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM](https://mlsanity.com/paper/2506.04593/) (2025)
- [PPO-Based Vehicle Control for Ramp Merging Scheme Assisted by Enhanced C-V2X](https://mlsanity.com/paper/2501.12656/) (2025)
- [Content Accuracy and Quality Aware Resource Allocation Based on LP-Guided DRL for ISAC-Driven AIGC Networks](https://mlsanity.com/paper/2508.12079/) (2025)
- [U-Parking: Distributed UWB-Assisted Autonomous Parking System with Robust Localization and Intelligent Planning](https://mlsanity.com/paper/2603.04898/) (2026)
- [PPO-Based Hybrid Optimization for RIS-Assisted Semantic Vehicular Edge Computing](https://mlsanity.com/paper/2603.09082/) (2026)

## Abstract (arXiv)

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.