Flad: Federated Learning Framework Enables Autonomous Driving with Cloud-Edge-Vehicle Collaboration

Large Language Models demonstrate considerable promise for autonomous driving, but training these complex systems presents challenges related to data transmission costs and data privacy. To address these issues, Tianao Xiang, Mingjian Zhi, and colleagues from Northeastern University, alongside Lin Cai and Yuhao Chen from the University of Victoria, present a new Federated Learning framework called FLAD. This innovative system enables autonomous vehicles to collaboratively train models without directly sharing sensitive driving data, utilising a cloud-edge-vehicle architecture to minimise delays and preserve privacy. By intelligently parallelising training and employing a knowledge distillation method, FLAD optimises efficiency and personalises models according to diverse data sources, representing a significant step towards future collaborative autonomous driving and knowledge sharing.

Federated Learning for Autonomous Vehicle AI

This extensive text details research and development related to Federated Learning and distributed training for autonomous driving, addressing data scarcity, privacy concerns, and substantial computational demands. It also addresses the need for real-time performance, crucial for the low-latency decision-making required by autonomous vehicles. The core of the proposed solution lies in Federated Learning, a distributed learning paradigm where models are trained collaboratively on decentralized data sources, such as vehicles, without sharing the raw data itself. To accelerate model training, the team utilizes distributed training techniques, including data parallelism and model parallelism.

Pipeline parallelism further enhances efficiency by dividing the model into stages and processing data in a continuous pipeline. Edge computing plays a vital role, deploying AI models and performing inference on edge devices like vehicles and roadside units to reduce latency and improve responsiveness. Resource optimization techniques, such as model compression, quantization, and pruning, efficiently utilize the limited resources available on these edge devices. This research showcases a shift towards decentralized, edge-based AI systems for autonomous driving, driven by the need for privacy, efficiency, and real-time performance.

Federated Learning for Autonomous Vehicle Control

The research team developed Federated LLM-based Autonomous Driving, or FLAD, a system designed to collaboratively train large language models for autonomous vehicles without directly sharing sensitive driving data. This work addresses high computational costs and privacy concerns inherent in traditional centralized training approaches, employing a cloud-edge-vehicle collaborative architecture to reduce communication delays and enhance data privacy. To overcome resource limitations, the team engineered an intelligent parallelized collaborative training method that optimizes training efficiency by dynamically scheduling communication and distributing computational workloads. The system harnesses the collective processing power of multiple end-devices, enabling the training of complex models that would otherwise be impractical, prioritizing data exchange and minimizing network congestion.

Furthermore, scientists implemented a knowledge distillation method to personalize the large language model according to the unique characteristics of data collected at each vehicle. This technique transfers knowledge from a larger model to a smaller, more efficient model suitable for deployment on resource-constrained edge devices, improving overall performance and robustness. To validate the system, the team prototyped FLAD using NVIDIA Jetson platforms, overcoming practical implementation challenges related to CPU/GPU memory sharing, dynamic model partitioning, and fault-tolerant training.

Federated Learning Accelerates Autonomous Vehicle Training

Scientists have developed Federated LLM-based Autonomous Driving, or FLAD, a groundbreaking framework that enables collaborative training of large language models for self-driving vehicles without directly sharing sensitive driving data. FLAD utilizes a three-layer architecture, vehicle, edge, and cloud, to distribute the training process and optimize resource utilization, addressing high computational demands, privacy concerns, and the limitations of resource-constrained onboard hardware. Experiments demonstrate that FLAD achieves 70% of the throughput of traditional centralized training methods, maintaining robust performance even with fluctuating network conditions and vehicle mobility. The system overcomes instabilities in vehicle availability by intelligently managing training cohorts across rounds, optimizing communication strategies within the framework to address competitive model transmission.

Furthermore, FLAD addresses imbalances in computational workload across vehicles with varying capabilities, ensuring efficient resource allocation and preventing bottlenecks. The team successfully mitigated hardware memory constraints by leveraging parameter-efficient fine-tuning techniques that modify only 0. 1-1% of model parameters, allowing for on-vehicle model personalization even with limited onboard memory. By combining federated learning principles with the multimodal reasoning capabilities of large language models, FLAD delivers a privacy-preserving system capable of interpreting complex traffic scenarios.

Federated Learning Improves Autonomous Driving Performance

This research presents Federated LLM-based Autonomous Driving, or FLAD, a new framework designed to enable collaborative training of large language models for autonomous vehicles. FLAD addresses key challenges in this field, including the high computational demands of training these models and the need to protect the privacy of sensitive driving data, employing a cloud-edge-vehicle collaborative architecture, intelligent parallelized training, and a knowledge distillation method that personalizes models to individual vehicle data. Experimental results demonstrate that FLAD successfully enhances driving performance while efficiently utilizing the distributed computing resources available in vehicles, opening up new possibilities for scalable and privacy-preserving deployment of autonomous driving systems. The researchers acknowledge that data loading speed and wireless network overhead currently limit FLAD’s performance, with future work focusing on developing more efficient models, optimizing data loading processes, and compressing communication overhead. Ultimately, this work demonstrates the potential of leveraging limited onboard resources, combined with edge and cloud computing, to enable complex autonomous tasks and extends to applications beyond vehicles, including drones, UAVs, and robots.