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With a massive influx of terminal devices connecting to wireless network, the rapid growth in wireless network traffic poses significant challenges to existing network architectures. Device-to-Device (D2D) communication facilitates direct data exchanges between devices, reducing latency and network congestion. However, implementing D2D communication technology in edge caching poses privacy and security concerns. This paper proposes a privacy-preserving edge caching algorithm based on permissioned blockchain and federated reinforcement learning (PBFRL) to ensure data privacy and security. The PBFRL algorithm incorporates a permissioned blockchain deployed on both base stations and user devices to ensure data privacy and security. Considering the varied hardware resources of user devices, an asynchronous training method is introduced, separating the sample collection algorithm and the updating network algorithm to run on different devices. Simulation results demonstrate that the proposed algorithm surpasses other benchmark algorithms in terms of content transmission delay and data privacy and security.

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A Privacy-Preserving Edge Caching Algorithm Based on Permissioned Blockchain and Federated Reinforcement Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Zicheng Luo,
  • Zhenchun Wei,
  • Zengwei Lyu,
  • Xiaohui Yuan,
  • Lin Feng,
  • Zhen Wei

摘要

With a massive influx of terminal devices connecting to wireless network, the rapid growth in wireless network traffic poses significant challenges to existing network architectures. Device-to-Device (D2D) communication facilitates direct data exchanges between devices, reducing latency and network congestion. However, implementing D2D communication technology in edge caching poses privacy and security concerns. This paper proposes a privacy-preserving edge caching algorithm based on permissioned blockchain and federated reinforcement learning (PBFRL) to ensure data privacy and security. The PBFRL algorithm incorporates a permissioned blockchain deployed on both base stations and user devices to ensure data privacy and security. Considering the varied hardware resources of user devices, an asynchronous training method is introduced, separating the sample collection algorithm and the updating network algorithm to run on different devices. Simulation results demonstrate that the proposed algorithm surpasses other benchmark algorithms in terms of content transmission delay and data privacy and security.