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• W4226256116 abstract "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." @default.
• W4226256116 created "2022-05-05" @default.
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• W4226256116 date "2022-01-01" @default.
• W4226256116 modified "2023-09-30" @default.
• W4226256116 title "Route-Based Proactive Content Caching Using Self-Attention in Hierarchical Federated Learning" @default.
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• W4226256116 doi "https://doi.org/10.1109/access.2022.3157637" @default.
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