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<p>Vehicular edge computing (VEC) supports real-time vehicular application services. However, accurate prediction and caching of popular content in roadside units (RSUs) while safeguarding user privacy is challenging. This study proposes a privacy-preserving cooperative hierarchical caching approach based on federated deep reinforcement learning (PCFR) for VEC. An asynchronous federated learning algorithm based on improved differential privacy is proposed, which considers the vehicle locations and movement directions and reasonably limits the local-update norm, improving the global model prediction accuracy while protecting user privacy. To address spatiotemporal variations in content popularity, a proposed attention-weighted asynchronous actor-critic collaborative caching algorithm extracts and weights key state features to optimize the collaborative cache content and its distribution location, enhancing the overall caching efficiency. In simulation, the PCFR scheme outperforms other caching schemes. With a 400-MB cache capacity, the PCFR scheme improves the cache hit rate by approximately 50.0% and reduces the content access delay by approximately 28.0%.</p>

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Privacy-preserving cooperative hierarchical caching approach based on federated deep reinforcement learning for vehicular edge computing

  • 期刊论文

  • 发表日期：

    2025年2月24日

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  • Yangxi Mu,
  • Bing Hao,
  • Mengyang He

摘要

Vehicular edge computing (VEC) supports real-time vehicular application services. However, accurate prediction and caching of popular content in roadside units (RSUs) while safeguarding user privacy is challenging. This study proposes a privacy-preserving cooperative hierarchical caching approach based on federated deep reinforcement learning (PCFR) for VEC. An asynchronous federated learning algorithm based on improved differential privacy is proposed, which considers the vehicle locations and movement directions and reasonably limits the local-update norm, improving the global model prediction accuracy while protecting user privacy. To address spatiotemporal variations in content popularity, a proposed attention-weighted asynchronous actor-critic collaborative caching algorithm extracts and weights key state features to optimize the collaborative cache content and its distribution location, enhancing the overall caching efficiency. In simulation, the PCFR scheme outperforms other caching schemes. With a 400-MB cache capacity, the PCFR scheme improves the cache hit rate by approximately 50.0% and reduces the content access delay by approximately 28.0%.