将多模态多任务联邦基础模型引入车联网，实现智能与隐私的统一。
Federated Foundation Models over Vehicular Networks
- 提出多模态多任务联邦基础模型的训练与微调方法。
- 在真实车载数据集上验证模型潜力，支持下一代车联网智能。
- 针对车辆环境挑战提出研究方向，开源代码促进后续研究。
本文提出将新兴的多模态多任务联邦基础模型（M3T FedFMs）融入车联网的前瞻性构想，旨在结合多模态多任务基础模型（M3T FMs）的强大表达能力与联邦学习（FL）的隐私保护和分布式学习优势。鉴于该研究方向尚处探索阶段，本文首先介绍M3T FedFMs的基础训练/微调原则，随后探讨其在车联网中的代表性应用场景，展示其在推动下一代车联网智能方面的巨大潜力。接着，识别出车联网环境中制约M3T FedFMs实际部署的关键限制，并提出一系列前瞻性研究方向以应对挑战。此外，基于真实车载数据集（Waymo Open Dataset）的案例研究，验证了M3T FedFMs在车联网中的可行性，并公开实现代码（仓库地址：https://github.com/KasraBorazjani/vehicular-fedfm），以促进可复现性并激发该新兴领域的研究活力。
原文摘要 · Abstract (English)
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)
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