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Mobile edge computing (MEC) is now being utilized to address the growing demand for edge devices and high-throughput, low-latency computational tasks. Users can offload tasks to MEC servers, significantly reducing latency and energy consumption. However, traditional single-access-point networks often struggle to meet the needs of a large number of users, and issues such as privacy leakage during the offloading process and information theft on edge servers are frequently overlooked. This paper proposes a multi-access-point offloading framework and a privacy-preserving quality of service (QoS) model. We construct the framework and model, assess privacy risks and protection levels, and integrate blockchain technology to enhance information security. By comprehensively considering user experience and privacy protection, we model the problem as a Markov decision process (MDP). Furthermore, a multi-agent proximal policy optimization (MAPPO) algorithm is proposed to achieve the optimal offloading solution. Simulation results validate the effectiveness of the proposed algorithm.

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Privacy-Preserving Offloading for Mobile Edge Computing: A Deep Reinforcement Learning Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Qizhuo Yang,
  • Yanhua Sun,
  • Jiali Li,
  • Zhuwei Wang,
  • Chao Fang,
  • Yang Sun

摘要

Mobile edge computing (MEC) is now being utilized to address the growing demand for edge devices and high-throughput, low-latency computational tasks. Users can offload tasks to MEC servers, significantly reducing latency and energy consumption. However, traditional single-access-point networks often struggle to meet the needs of a large number of users, and issues such as privacy leakage during the offloading process and information theft on edge servers are frequently overlooked. This paper proposes a multi-access-point offloading framework and a privacy-preserving quality of service (QoS) model. We construct the framework and model, assess privacy risks and protection levels, and integrate blockchain technology to enhance information security. By comprehensively considering user experience and privacy protection, we model the problem as a Markov decision process (MDP). Furthermore, a multi-agent proximal policy optimization (MAPPO) algorithm is proposed to achieve the optimal offloading solution. Simulation results validate the effectiveness of the proposed algorithm.