Federated Foundation Models over Vehicular Networks

2026-06-04

AI Summary

This work proposes the first Multimodal Multitask Federated Foundation Model framework for vehicular networks (M3T FedFMs), addressing the dual challenges of privacy preservation and intelligent adaptation in dynamic vehicular environments. By integrating the powerful representation capabilities of foundation models with the privacy-preserving distributed nature of federated learning, M3T FedFMs leverages multimodal perception, multitask learning, and distributed optimization to enable efficient collaborative training and inference in real-world vehicular scenarios. Experimental validation on the Waymo Open Dataset demonstrates the feasibility and performance advantages of the proposed framework. The study further provides a systematic analysis of key deployment challenges and releases open-source code to support reproducibility, thereby establishing a foundational benchmark for this emerging research direction.

Abstract

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)

Problem

• Federated Learning
• Foundation Models
• Vehicular Networks
• Multi-modal Learning
• Multi-task Learning

Innovation

• Federated Foundation Models
• Multi-modal Multi-task Learning
• Vehicular Networks
• Privacy-preserving AI
• Distributed Learning