In recent years, autonomous driving has garnered significant attention due to
its potential for improving road safety through collaborative perception among
connected and autonomous vehicles (CAVs). However, time-varying channel
variations in vehicular transmission environments demand dynamic allocation of
communication resources. Moreover, in the context of collaborative perception,
it is important to recognize that not all CAVs contribute valuable data, and
some CAV data even have detrimental effects on collaborative perception. In
this paper, we introduce SmartCooper, an adaptive collaborative perception
framework that incorporates communication optimization and a judger mechanism
to facilitate CAV data fusion. Our approach begins with optimizing the
connectivity of vehicles while considering communication constraints. We then
train a learnable encoder to dynamically adjust the compression ratio based on
the channel state information (CSI). Subsequently, we devise a judger mechanism
to filter the detrimental image data reconstructed by adaptive decoders. We
evaluate the effectiveness of our proposed algorithm on the OpenCOOD platform.
Our results demonstrate a substantial reduction in communication costs by
23.10\% compared to the non-judger scheme. Additionally, we achieve a
significant improvement on the average precision of Intersection over Union
(AP@IoU) by 7.15\% compared with state-of-the-art schemes.

SmartCooper 是一个自适应协同感知框架，结合了通信优化和判决机制，以提高车辆之间的连接性和共享感知数据的质量。在 OpenCOOD 平台上的评估结果显示，相比非判决方案，我们的算法成功降低了 23.10% 的通信成本，并将 Intersection over Union (IoU) 的平均精度 (AP@IoU) 提高了 7.15%。

智能协同：车载自适应融合感知与判决机制

SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and  Judger Mechanism

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%.

本文介绍了 CRCHFL 框架来优化通信资源限制下的层次化联邦学习，以最小化混合数据和模型聚合下自主驾驶模型的推理误差，并在 CARLA 仿真平台上进行了验证，结果表明，CRCHFL 不仅加速了收敛速度，而且比 HFL 和 SFL 表现更优。

面向端到端自主驾驶的通信资源受限分层联邦学习

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

Modern Deep Neural Networks (DNNs) require significant memory to store
weight, activations, and other intermediate tensors during training. Hence,
many models do not fit one GPU device or can be trained using only a small
per-GPU batch size. This survey provides a systematic overview of the
approaches that enable more efficient DNNs training. We analyze techniques that
save memory and make good use of computation and communication resources on
architectures with a single or several GPUs. We summarize the main categories
of strategies and compare strategies within and across categories. Along with
approaches proposed in the literature, we discuss available implementations.

本文系统地讨论了提高深度神经网络训练效率的方法，重点考虑了内存利用率和 GPU 训练，分类总结了相关策略，并且比较了不同类别之间的方法。

大规模神经网络训练调查

Survey on Large Scale Neural Network Training

Federated learning becomes increasingly attractive in the areas of wireless
communications and machine learning due to its powerful functions and potential
applications. In contrast to other machine learning tools that require no
communication resources, federated learning exploits communications between the
central server and the distributed local clients to train and optimize a
machine learning model. Therefore, how to efficiently assign limited
communication resources to train a federated learning model becomes critical to
performance optimization. On the other hand, federated learning, as a brand new
tool, can potentially enhance the intelligence of wireless networks. In this
article, we provide a comprehensive overview on the relationship between
federated learning and wireless communications, including basic principle of
federated learning, efficient communications for training a federated learning
model, and federated learning for intelligent wireless applications. We also
identify some future research challenges and directions at the end of this
article.

本篇文章探讨了联邦学习与无线通信之间的关系，包括联邦学习的基本原理、训练模型的有效通信、联邦学习对智能无线应用的潜在贡献以及未来研究挑战和方向。