Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning

Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL...

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Veröffentlicht in:IEEE journal of selected topics in signal processing Jg. 17; H. 1; S. 66 - 81
Hauptverfasser: Wu, Qiong, Zhao, Yu, Fan, Qiang, Fan, Pingyi, Wang, Jiangzhou, Zhang, Cui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Asynchronous federated learning
Caching
Computational modeling
Cooperative caching
Deep learning
deep reinforcement learning
Delays
Edge computing
Fans
Machine learning
Model accuracy
Prediction algorithms
Real-time systems
Reinforcement learning
Roadsides
VEC
Vehicles
ISSN:1932-4553, 1941-0484
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Zusammenfassung:Vehicular edge computing (VEC) can learn and cache most popular contents for vehicular users (VUs) in the roadside units (RSUs) to support real-time vehicular applications. Federated learning (FL) can protect VUs' privacy by sharing vehicles' local models instead of data. In traditional FL, the global model is periodically updated by aggregating all vehicles' local models. However, vehicles may frequently drive out of the coverage area of VEC before they finish the local model training and thus the traditional FL cannot upload all local models as expected, which would degrade the accuracy of global model. The asynchronous FL can be performed without aggregating all vehicles' local models, thus more local models can be uploaded to improve the accuracy of global model. The vehicle mobility significantly impacts the asynchronous FL. There is no published work considering the vehicle mobility to design the cooperative caching in VEC based on asynchronous FL. In addition, the caching capacity of RSU is limited and the size of the predicted popular contents usually exceeds the cache capacity of RSU. Hence, VEC should cache the predicted popular contents in different RSUs while considering content transmission delay. In this paper, we consider vehicle mobility and propose a cooperative caching scheme in the VEC based on asynchronous federated and deep reinforcement learning (CAFR) to predict popular contents and further obtain the optimal cooperative caching location for the predicted popular contents. Extensive experimental results have demonstrated that CAFR scheme outperforms other baseline caching schemes.
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ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2022.3221271