FLAD: Federated-Trained Large Language Models for Autonomous Driving
Bibliographic record
Abstract
Large language models (LLMs) have demonstrated advanced capabilities in contextual understanding, multi-modal fusion and cross-domain reasoning, and improved decision-making and planning, making them a promising technology for advancing autonomous driving (AD). However, training and deploying LLM-based AD systems face significant challenges: high computation requirements, data privacy concerns, and the need for personalization across diverse driving environments. In this paper, we present Federated-Trained LLM for Autonomous Driving (FLAD), a novel framework that leverages distributed intelligence across vehicles, edge servers, and cloud infrastructure to address the above challenges. FLAD introduces three key innovations: 1) a cloud-edge-vehicle collaborative architecture that balances computational demands while preserving data privacy; 2) an intelligent training system that enables resource-constrained vehicles to participate in model development through optimized workload distribution; and 3) a knowledge distillation approach that personalizes models for specific regions. We deploy a working implementation to illustrate how to address practical deployment practical challenges. Then, we use the implementation to demonstrate how FLAD improves autonomous driving performance while efficiently utilizing distributed resources. This article sheds new light on how to apply privacy- preserving federated training on LLMs to enhance autonomous driving systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".