GIoV: Achieving Generative AI Services in Internet of Vehicles via Collaborative Edge Intelligence

• Published in: 2024 IEEE Wireless Communications and Networking Conference (WCNC) pp. 1 - 6
• Main Authors: Xie, Gaochang, Xie, Renchao, Zhang, Xinyuan, Nie, Jiangtian, Tang, Qinqin, Bryan Lim, Wei Yang, Niyato, Dusit
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21-04-2024
• Subjects: Collaboration; collaborative fine-tuning; Computational modeling; edge intelligence; Energy consumption; Federated learning; Generative AI; Generative artificial intelligence (GAl); Image edge detection; Internet of Vehicles (10 V); resource allocation; text-to-image diffusion model; Wireless communication
• ISSN: 1558-2612
• DOI: 10.1109/WCNC57260.2024.10571334

The utilization of emergent Generative Artificial Intelligence (GAl) within the realm of Internet of Vehicles (loV) can augment edge intelligence, thereby catering to the diverse content-generation needs of novel in-vehicle services. Nonethe-less, existing cloud-centric GAl paradigms are not inherently suitable for wireless vehicular networks, primarily due to their extensive computing requirements, lack of specificity, and spatial detachment from end users. To cope with these challenges, we introduce an innovative Generative 10 V (g 10 V)architecture that employs a collaborative fine-tuning mechanism for pre-trained GAl models. The mechanism is mainly orchestrated collaboratively by Road-Side Units (RSUs) and vehicles within a Federated Learning (FL) paradigm. Here, we take text-to-image diffusion models as typical examples to show the co-fine-tuning workflow in detail, aiming to utilize edge traffic data to realize rapid, customized, and lightweight GAl in the resource-limited 10 V scenario. Thereafter, we formulate the problem of edge communication and computation resource allocation during RSU-vehicle co-fine-tuning, which is pivotal for optimizing time and energy consumption within this process. To address the challenge, we deploy a Self-adaptive Harmony Search (SHS)-based resource allocation strategy. Experiments based on Stable Diffusion vl-4 model validate the excellent performance in image generating and the time and energy consumption during co-fine-tuning in resource-limited and fast-changing 10 V scenarios.