arXiv:2606.06786v1 Announce Type: new 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 representati
The convergence of advanced AI models (foundation models) and the increasing computational capabilities of edge devices like vehicles makes this integration timely.
This research outlines a pathway for distributed, privacy-preserving AI in mobile contexts, potentially transforming data collection, processing, and application within smart cities and autonomous systems.
The ability to run sophisticated multi-modal and multi-task AI models directly within vehicular networks, leveraging federated learning for privacy and efficiency, changes how AI can be deployed and scaled in dynamic environments.
- · Automotive industry
- · Edge AI providers
- · Smart city developers
- · Cybersecurity sector
- · Centralized cloud AI services (in specific use cases)
- · Legacy in-vehicle software providers
Vehicles gain enhanced autonomous capabilities and contextual awareness through shared, privacy-preserving AI models.
This decentralized AI approach could accelerate the development of truly autonomous transportation systems and smart infrastructure.
The widespread deployment of such models might necessitate new regulatory frameworks for data sharing, liability, and ethical AI in mobility.
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