RCFL: Redundancy-Aware Collaborative Federated Learning in Vehicular Networks

[PDF] mdpi.com

Network Latency in Teleoperation of Connected and Autonomous Vehicles: A Review of Trends, Challenges, and Mitigation Strategies

SB Kamtam, Q Lu, F Bouali, OCL Haas, S Birrell - Sensors, 2024 - mdpi.com
With remarkable advancements in the development of connected and autonomous vehicles
(CAVs), the integration of teleoperation has become crucial for improving safety and …
被引用次数：5 相关文章
[PDF] arxiv.org

pfedlvm: A large vision model (lvm)-driven and latent feature-based personalized federated learning framework in autonomous driving

WB Kou, Q Lin, M Tang, S Xu, R Ye, Y Leng… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization
due to data heterogeneity in an ever domain-shifting environment. While Federated …
被引用次数：5 相关文章 所有 3 个版本

Prevent Deception: On-Demand Data Synchronization for Vehicle Digital Twins

Y Hui, Y Li, N Cheng, C Li, C Zhou… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
In digital-twin-enabled heterogeneous vehicular networks (DT-HetVNets), vehicles need to
synchronize data to their DTs deployed in the cloud for decision-making. However, for a …
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[PDF] arxiv.org

Fast-Convergent and Communication-Alleviated Heterogeneous Hierarchical Federated Learning in Autonomous Driving

WB Kou, Q Lin, M Tang, R Ye, S Wang, G Zhu… - arXiv preprint arXiv …, 2024 - arxiv.org
Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous
driving (AD). However, inference model trained from data in a particular geographical region …
相关文章 所有 3 个版本