Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning
Mobility-Aware Proactive Edge Caching for Connected Vehicles Using Federated Learning
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DOI：
10.1109/tits.2020.3017474
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发表时间：
2021-08
影响因子：
8.5
通讯作者：
Zhengxin Yu;Jia Hu;G. Min;Zhiwei Zhao;W. Miao;M. S. Hossain
中科院分区：
文献类型：
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作者：
Zhengxin Yu;Jia Hu;G. Min;Zhiwei Zhao;W. Miao;M. S. Hossain
车辆网络边缘的内容缓存被认为是一种有前景的技术，可以满足智能交通中计算密集型和延迟敏感型车辆应用日益增长的需求。现有的内容缓存方案在用于车辆网络时面临两个不同的挑战：1）连接到边缘服务器的车辆不断移动，使得内容流行度变化且难以预测。 2) 缓存内容很容易过时，因为每辆联网车辆都会在边缘服务器区域停留很短的时间。为了应对这些挑战，我们提出了一种基于联邦学习（MPCF）的移动感知主动边缘缓存方案。这种新方案使多辆车能够协作学习全局模型，通过本地车辆上分布的私人训练数据来预测内容受欢迎程度。 MPCF 还采用上下文感知对抗自动编码器来预测高度动态的内容流行度。此外，MPCF 集成了移动感知缓存替换策略，允许网络边缘根据车辆的移动模式和偏好添加/删除内容。 MPCF可以大幅提升缓存性能，有效保护用户隐私，并显着降低通信成本。实验结果表明，MPCF 在车辆边缘网络中的缓存命中率方面优于其他基线缓存方案。
Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users’ privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.