# Open-Source LLM-Driven Federated Transformer for Predictive IoV Management

arXiv: https://arxiv.org/abs/2505.00651 · HTML: https://mlsanity.com/paper/2505.00651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 4 of 4)

- [LLMs meet Federated Learning for Scalable and Secure IoT Management](https://mlsanity.com/paper/2504.16032/) (2025)
- [Mobility-Aware Decentralized Federated Learning with Joint Optimization of Local Iteration and Leader Selection for Vehicular Networks](https://mlsanity.com/paper/2503.06443/) (2025)
- [Homomorphic Encryption-Enabled Federated Learning for Privacy-Preserving Intrusion Detection in Resource-Constrained IoV Networks](https://mlsanity.com/paper/2407.18503/) (2024)
- [Real-World Data Inspired Interactive Connected Traffic Scenario Generation](https://mlsanity.com/paper/2409.17429/) (2024)

## Built on by (most cited first, 1 of 1)

- [Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models](https://mlsanity.com/paper/2507.07406/) (2025)

## Abstract (arXiv)

The proliferation of connected vehicles within the Internet of Vehicles (IoV) ecosystem presents critical challenges in ensuring scalable, real-time, and privacy-preserving traffic management. Existing centralized IoV solutions often suffer from high latency, limited scalability, and reliance on proprietary Artificial Intelligence (AI) models, creating significant barriers to widespread deployment, particularly in dynamic and privacy-sensitive environments. Meanwhile, integrating Large Language Models (LLMs) in vehicular systems remains underexplored, especially concerning prompt optimization and effective utilization in federated contexts. To address these challenges, we propose the Federated Prompt-Optimized Traffic Transformer (FPoTT), a novel framework that leverages open-source LLMs for predictive IoV management. FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction. The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence. A Transformer-driven synthetic data generator is incorporated to augment training with diverse, high-fidelity traffic scenarios in the Next Generation Simulation (NGSIM) format. Extensive evaluations demonstrate that FPoTT, utilizing EleutherAI Pythia-1B, achieves 99.86% prediction accuracy on real-world data while maintaining high performance on synthetic datasets. These results underscore the potential of open-source LLMs in enabling secure, adaptive, and scalable IoV management, offering a promising alternative to proprietary solutions in smart mobility ecosystems.