Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving

2023-06-28 · Wei-Bin Kou, Shuai Wang, Guangxu Zhu, Bin Luo, Yingxian Chen, Derrick Wing Kwan Ng, Yik-Chung Wu

While federated learning (FL) improves the generalization of end-to-end autonomous driving by model aggregation, the conventional single-hop FL (SFL) suffers from slow convergence rate due to long-range communications among vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such drawbacks via introduction of mid-point edge servers. However, the orchestration between constrained communication resources and HFL performance becomes an urgent problem. This paper proposes an optimization-based Communication Resource Constrained Hierarchical Federated Learning (CRCHFL) framework to minimize the generalization error of the autonomous driving model using hybrid data and model aggregation. The effectiveness of the proposed CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform. Results show that the proposed CRCHFL both accelerates the convergence rate and enhances the generalization of federated learning autonomous driving model. Moreover, under the same communication resource budget, it outperforms the HFL by 10.33% and the SFL by 12.44%.

📄 PDF Abstract BibTeX arXiv:2306.16169

Code (1)

weibinkou/crchfl 공식 구현 pytorch

Tasks

Autonomous Driving Federated Learning

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

2025-11-12 · Tianao Xiang, Mingjian Zhi, Yuanguo Bi, Lin Cai 외 arxiv

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, a…

Knowledge Distillation Autonomous Vehicles Federated Learning Autonomous Driving

PFAdapter: Hierarchical LoRA Decomposition for Personalized Federated MLLMs

2026-07-13 · Jing Liu, Kun Yang, Yan Wang, Dingkang Yang 외 arxiv

Agentic AI systems are reshaping communications and networking by deploying autonomous intelligent agents capable of collaborative learning while maintaining data privacy at network edges. Within distributed network envi…

CoCoFL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization

2022-03-10 · Kilian Pfeiffer, Martin Rapp, Ramin Khalili, Jörg Henkel

Devices participating in federated learning (FL) typically have heterogeneous communication, computation, and memory resources. However, in synchronous FL, all devices need to finish training by the same deadline dictate…

Fairness Federated Learning Quantization

Experience Constrained Hierarchical Federated Reinforcement Learning for Large-scale UAV Teams in Hazardous Environments

2026-05-04 · Qinwei Huang, Rui Zuo, Simon Khan, Qinru Qiu arxiv

Conventional federated learning assumes that greater learner participation improves training performance, by leveraging abundant, independently generated local data. However, in federated reinforcement learning (FRL) for…

Reinforcement Learning Federated Learning

Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edge

2025-03-27 · Wanli Ni, Haofeng Sun, Huiqing Ao, Hui Tian

Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due to issues such as data privacy, computat…