This paper presents a forward-looking vision for integrating the emerging multi-modal multi-task federated foundation models (M3T FedFMs) into vehicular networks, with the goal of unifying the expressive power of multi-modal multi-task foundation models (M3T FMs) with the privacy-preserving and distributed learning capabilities of federated learning (FL). Given the largely underexplored nature of this research direction, we first introduce the fundamental training/fine-tuning principles of M3T FedFMs. We then discuss a range of their representative use cases in vehicular networks, illustrating the significant potential of M3T FedFMs to enable next-generation vehicular intelligence. Afterwards, we identify key constraints inherent to vehicular environments that challenge the practical deployment of M3T FedFMs, and articulate a set of forward-looking research directions to address these challenges. Furthermore, through a case study conducted on a real-world vehicular dataset (i.e., Waymo Open Dataset), we demonstrate the promise of M3T FedFMs for vehicular networks and release our implementation to facilitate reproducibility and stimulate research in this emerging area (repository: https://github.com/KasraBorazjani/vehicular-fedfm)

翻译：本文提出了一种前瞻性愿景，旨在将新兴的多模态多任务联邦基础模型（M3T FedFMs）集成到车载网络中，目标是将多模态多任务基础模型（M3T FMs）的表达能力与联邦学习（FL）的隐私保护及分布式学习能力相统一。鉴于该研究方向在很大程度上尚未被探索，我们首先介绍了M3T FedFMs的基本训练/微调原则。随后，我们讨论了其在车载网络中的一系列代表性用例，展示了M3T FedFMs在实现下一代车载智能方面的巨大潜力。接着，我们指出了车载环境中固有的关键约束，这些约束对M3T FedFMs的实际部署构成了挑战，并阐明了一系列前瞻性研究方向以应对这些挑战。此外，通过在实际车载数据集（即Waymo Open Dataset）上进行的案例研究，我们证明了M3T FedFMs在车载网络中的应用前景，并公开了我们的实现代码（仓库地址：https://github.com/KasraBorazjani/vehicular-fedfm），以促进可重复性并激发该新兴领域的研究。