基于自适应模型剪枝的时延受限车联网高效联邦学习

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Xing Chang;Mohammad S. Obaidat;Jingxiao Ma;Xiaoping Xue;Yantao Yu;Xuewen Wu
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引用次数: 0

摘要

在车联网(IoV)中,数据隐私问题促使采用了联邦学习(FL)。提高FL的效率仍然是研究的重点领域,最近的研究探索了模型修剪以减少计算和通信开销。然而,在车联网中,模型修剪提出了独特的挑战,并且仍未得到充分的探索。修剪策略的设计至关重要,因为它直接影响到每个车辆的学习延迟和参与FL的能力。此外,FL性能和模型修剪是错综复杂的联系。此外,每轮车辆数量和移动状态的波动使最佳修剪比的确定复杂化,修剪与车辆选择紧密交织在一起。本研究引入了车辆联邦学习与自适应模型修剪(VFed-AMP),通过将自适应修剪与动态车辆选择和资源分配相结合来解决这些挑战。我们分析了剪枝率对学习延迟和收敛速度的影响。然后,在这些发现的指导下,制定了一个联合优化问题,以最大限度地提高最优车辆选择,带宽分配和修剪比率的收敛速度。最后,提出了一种低复杂度的自适应剪枝和车辆调度联合算法来解决这一问题。通过理论分析和系统设计,VFed-AMP提高了IoV中FL的效率和可扩展性,为通过战略性模型调整优化FL性能提供了见解。不同数据集上的数值结果表明,与传统的FL方法相比,VFed-AMP实现了卓越的训练精度(例如,比利时ts至少提高了13.4%),并显着减少了训练时间(例如,CIFAR-10至少减少了1.8倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Efficient Federated Learning via Adaptive Model Pruning for Internet of Vehicles With a Constrained Latency
In the Internet of Vehicles (IoV), data privacy concerns have prompted the adoption of Federated Learning (FL). Efficiency improvements in FL remain a focal area of research, with recent studies exploring model pruning to lessen both computation and communication overhead. However, in the IoV, model pruning presents unique challenges and remains underexplored. Pruning strategy design is critical as it directly impacts each vehicle's learning latency and capacity to participate in FL. Furthermore, FL performance and model pruning are intricately connected. Additionally, the fluctuating number and mobility states of vehicles per round complicate determining the optimal pruning ratio, closely intertwining pruning with vehicle selection. This study introduces Vehicular Federated Learning with Adaptive Model Pruning (VFed-AMP) to tackle these challenges by integrating adaptive pruning with dynamic vehicle selection and resource allocation. We analyze the impact of pruning ratios on learning latency and convergence rate. Then, guided by these findings, a joint optimization problem is formulated to maximize the convergence rate concerning optimal vehicle selection, bandwidth allocation, and pruning ratios. Finally, a low-complexity algorithm for joint adaptive pruning and vehicle scheduling is proposed to address this problem. Through theoretical analysis and system design, VFed-AMP enhances FL efficiency and scalability in the IoV, offering insights into optimizing FL performance through strategic model adjustments. Numerical results on various datasets show VFed-AMP achieves superior training accuracy (e.g., at least 13.4% improvement for BelgiumTS) and significantly reduces training time (e.g., at least up to $1.8\times$ for CIFAR-10) compared to traditional FL methods.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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