AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks

doi:10.3390/electronics15020479

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	AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks
	Tian, Yu 1,2; Wang, Nina 1,2,3,4; Zhang, Zongshuai 1,3,4; Zou, Wenhao 1,2; Zhao, Liangjie 1,2; Liu, Shiyao 1,2; Tian, Lin 1,2,3
	2026-01-22
发表期刊	ELECTRONICS
ISSN	2079-9292
卷号	15 期号:2 页码:28
摘要	Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Scheduling (EVS) configuration that keeps the number of participating edge nodes and vehicles per node constant across training rounds. However, given the diverse training tasks and dynamic vehicular environments, our experiments confirm that such static configurations struggle to efficiently meet the task-specific requirements across model accuracy, time delay, and energy consumption. To address this, we first formulate a unified, long-term training cost metric that balances these conflicting objectives. We then propose AptEVS, an adaptive scheduling framework based on deep reinforcement learning (DRL), designed to minimize this cost. The core of AptEVS is its phase-aware design, which adapts the scheduling strategy by first identifying the current training phase and then switching to specialized strategies accordingly. Extensive simulations demonstrate that AptEVS learns an effective scheduling policy online from scratch, consistently outperforming baselines and and reducing the long-term training cost by up to 66.0%. Our findings demonstrate that phase-aware DRL is both feasible and highly effective for resource scheduling over complex vehicular networks.
关键词	hierarchical federated learning deep reinforcement learning edge-and-vehicle scheduling vehicular networks
DOI	10.3390/electronics15020479
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering ; Physics
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Physics, Applied
WOS记录号	WOS:001670325700001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42854
专题	中国科学院计算技术研究所
通讯作者	Wang, Nina
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Univ Chinese Acad Sci, Nanjing 211135, Peoples R China 4.Nanjing Inst InforSuperBahn, Nanjing 211100, Peoples R China
推荐引用方式 GB/T 7714	Tian, Yu,Wang, Nina,Zhang, Zongshuai,et al. AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks[J]. ELECTRONICS,2026,15(2):28.
APA	Tian, Yu.,Wang, Nina.,Zhang, Zongshuai.,Zou, Wenhao.,Zhao, Liangjie.,...&Tian, Lin.(2026).AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks.ELECTRONICS,15(2),28.
MLA	Tian, Yu,et al."AptEVS: Adaptive Edge-and-Vehicle Scheduling for Hierarchical Federated Learning over Vehicular Networks".ELECTRONICS 15.2(2026):28.