Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning

IEEE Access

Subina Khanal¹  Eui-Nam Huh¹  Kyi Thar¹ 

[1] Department of Computer Science and Engineering, Kyung Hee University, Yongin-si, South Korea;

关键词: Proactive content caching;    multi-access edge computing;    federated learning;    self-attention mechanism;    recurrent neural network;   

DOI  :  10.1109/ACCESS.2022.3157637

来源: DOAJ

【 摘 要 】

The sheer unpredictability of content popularity, diversified user preferences and demands, and privacy concerns for data sharing all create hurdles to develop proactive content caching strategies in self-driving cars. Therefore, to address these concerns, we investigate in detail the role of proactive content caching methods in self-driving cars for improving quality-of-experience (QoE) and content retrieval cost in this work. We develop a low-complexity content popularity prediction mechanism in a hierarchical federated setting. In particular, we use a self-attention technique with an LSTM-based prediction mechanism to extract local content popularity patterns in self-driving cars. However, the local contents will not be sufficient to satisfy the passenger’s requirements. Hence, using the popular contents of other self-driving cars will solve the requirement constraint but poses some privacy issues. We use the privacy-preserving decentralized model training framework of Federated Learning (FL) to tackle this issue. Specifically, we deploy the hierarchical Federated Averaging (FedAvg) algorithm on local models obtained from self-driving cars to develop a regional and global content popularity prediction model at RSU and MBS, respectively. Extensive simulations on real-world datasets show the proposed approach improves cache space utilization by maximizing the local cache hit ratio, and further, minimizes the content retrieval cost for self-driving cars as compared with alternative methods.

【 授权许可】

Unknown