FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks

• URL: http://arxiv.org/abs/2511.09025v1
• Date: Thu, 13 Nov 2025 01:26:39 GMT
• Title: FLAD: Federated Learning for LLM-based Autonomous Driving in Vehicle-Edge-Cloud Networks
• Authors: Tianao Xiang, Mingjian Zhi, Yuanguo Bi, Lin Cai, Yuhao Chen,
• Abstract summary: Federated Learning (FL) is promising for enabling autonomous vehicles to collaboratively train models without sharing raw data.<n>We present Federated LLM-based Autonomous Driving (FLAD), an FL framework that leverages distributed multimodal sensory data across AVs in heterogeneous environment.<n>FLAD has three key innovations: (1) a cloud-edge-vehicle collaborative architecture that reduces communication delay and preserving data privacy; (2) an intelligent parallelized collaborative training with a communication scheduling mechanism that optimize training efficiency; and (3) a knowledge distillation method that personalizes LLM according to heterogeneous edge data.
• Score: 13.159277094011053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have impressive data fusion and reasoning capabilities for autonomous driving (AD). However, training LLMs for AD faces significant challenges including high computation transmission costs, and privacy concerns associated with sensitive driving data. Federated Learning (FL) is promising for enabling autonomous vehicles (AVs) to collaboratively train models without sharing raw data. We present Federated LLM-based Autonomous Driving (FLAD), an FL framework that leverages distributed multimodal sensory data across AVs in heterogeneous environment. FLAD has three key innovations: (1) a cloud-edge-vehicle collaborative architecture that reduces communication delay and preserving data privacy; (2) an intelligent parallelized collaborative training with a communication scheduling mechanism that optimizes training efficiency, leveraging end-devices otherwise having insufficient resources for model training; and (3) a knowledge distillation method that personalizes LLM according to heterogeneous edge data. In addition, we prototype FLAD in a testbed with NVIDIA Jetsons, overcoming practical implementation challenges including CPU/GPU memory sharing in resource-constrained devices, dynamic model partitions, and fault-tolerant training.Extensive experimental evaluation demonstrates that FLAD achieves superior end-to-end AD performance while efficiently utilizing distributed vehicular resources, opening up new possibilities for future collaborative AD model training and knowledge sharing.

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