Proactive Content Caching for Internet-of-Vehicles based on Peer-to-Peer Federated Learning
To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and r...
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| Published in: | Proceedings - International Conference on Parallel and Distributed Systems pp. 601 - 608 |
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| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
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01.12.2020
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| ISSN: | 2690-5965 |
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| Abstract | To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in machine learning, learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users' content retrieval history and personal data, leading to privacy concerns. To address the above challenge, we propose a new proactive caching scheme based on peer-to-peer federated deep learning, where the global prediction model is trained from data scattered at vehicles to mitigate the privacy risks. In our proposed scheme, a vehicle acts as a parameter server to aggregate the updated global model from peers, instead of an edge node. A dual-weighted aggregation scheme is designed to achieve high global model accuracy. Moreover, to enhance the caching performance, a Collaborative Filtering based Variational AutoEncoder model is developed to predict the content popularity. The experimental results demonstrate that our proposed caching scheme largely outperforms typical baselines, such as Greedy and Most Recently Used caching. |
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| AbstractList | To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in machine learning, learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users' content retrieval history and personal data, leading to privacy concerns. To address the above challenge, we propose a new proactive caching scheme based on peer-to-peer federated deep learning, where the global prediction model is trained from data scattered at vehicles to mitigate the privacy risks. In our proposed scheme, a vehicle acts as a parameter server to aggregate the updated global model from peers, instead of an edge node. A dual-weighted aggregation scheme is designed to achieve high global model accuracy. Moreover, to enhance the caching performance, a Collaborative Filtering based Variational AutoEncoder model is developed to predict the content popularity. The experimental results demonstrate that our proposed caching scheme largely outperforms typical baselines, such as Greedy and Most Recently Used caching. |
| Author | Yu, Zhengxin Hu, Jia Xu, Han Min, Geyong Mills, Jed |
| Author_xml | – sequence: 1 givenname: Zhengxin surname: Yu fullname: Yu, Zhengxin email: zy246@exeter.ac.uk organization: University of Exeter,Department of Computer Science,UK – sequence: 2 givenname: Jia surname: Hu fullname: Hu, Jia email: j.hu@exeter.ac.uk organization: University of Exeter,Department of Computer Science,UK – sequence: 3 givenname: Geyong surname: Min fullname: Min, Geyong email: g.min@exeter.ac.uk organization: University of Exeter,Department of Computer Science,UK – sequence: 4 givenname: Han surname: Xu fullname: Xu, Han email: hx230@exeter.ac.uk organization: University of Exeter,Department of Computer Science,UK – sequence: 5 givenname: Jed surname: Mills fullname: Mills, Jed email: jm729@exeter.ac.uk organization: University of Exeter,Department of Computer Science,UK |
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| Snippet | To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and... |
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| SubjectTerms | AutoEncoder Collaborative filtering Collaborative work Data privacy Deep Learning Edge Caching Federated Learning Internet of Vehicles Peer-to-peer computing Predictive models Servers Training |
| Title | Proactive Content Caching for Internet-of-Vehicles based on Peer-to-Peer Federated Learning |
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