Federated Foundation Models over Vehicular Networks

arXiv:2606.06786 · cs.LG, cs.NI · Submitted 2026-06-05 · Read on arXiv

cs.LG, cs.NI

Submitted: 2026-06-05

Updated: 2026-09-05

Comments: 8 pages, 4 figures

Code: https://github.com/KasraBorazjani/vehicular-fedfm

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fine-Tuning — 19× in this paper · explained in 5 episodes
• Foundation Model — 9× in this paper · explained in 3 episodes
• Downstream Tasks — 8× in this paper · explained in 2 episodes
• Foundation Models — 8× in this paper · explained in 7 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Adaptation — 7× in this paper · explained in 1 episode
• Aggregation — 7× in this paper · explained in 1 episode
• Embeddings — 7× in this paper · explained in 2 episodes
• Federated Learning — 6× in this paper · explained in 14 episodes
• Federated Learning (FL) — 3× in this paper · explained in 7 episodes
• Continual Learning — 2× in this paper · explained in 2 episodes
• Low-Rank Adaptation (LoRA) — 2× in this paper · explained in 9 episodes
• Mixture-of-Experts (MoE) — 2× in this paper · explained in 7 episodes
• Modular Architecture — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Endto-End Multimodal Model for Autonomous Driving — 1× in this paper
• Generative AI — 1× in this paper · explained in 2 episodes
• Large Vision Models — 1× in this paper
• Modularity — 1× in this paper · explained in 9 episodes
• No Task Adapter — 1× in this paper
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Steering — 1× in this paper · explained in 2 episodes

Sources

• Multi-modal Sensor Fusion for Auto Driving Perception: A Survey
• EMMA: End-to-End Multimodal Model for Autonomous Driving
• DeepInteraction++: Multi-Modality Interaction for Autonomous Driving
• pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
• DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

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