IRS-Assisted Secure Computation Offloading in Multi-Region Mobile Edge Computing via Multi-Agent Deep Reinforcement Learning

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Abstract
Mobile edge computing (MEC) offers an advanced solution to offload tasks from user devices (UDs) to the network edge, alleviating network congestion and reducing task execution latency. Nevertheless, current computation offloading still faces two critical challenges: high transmission latency due to poor wireless channel quality, and task privacy leakage due to the potential for eavesdropping. Intelligent reflecting surface (IRS) is a promising technique to enhance channel quality and security in MEC networks. In this paper, we consider a multi-region IRS-assisted MEC network that jointly optimizes computation offloading and IRS phase configuration to minimize the task execution latency for UDs. The optimization problem is NP-hard and requires distributed decision-making in multiple regions. Therefore, we propose a multi-agent deep reinforcement learning algorithm with an attention mechanism and Gumbel-Softmax. Specifically, the attention mechanism helps dynamically select key information and improve decision-making efficiency, while Gumbel-Softmax is used to solve the problem of the non-differentiable discrete action spaces. blackSimulation results demonstrate that the proposed algorithm outperforms baseline schemes by reducing the task execution latency and improving the offloading rate by 27.2% and 60.7% on average in the considered MEC networks.
Funding Information
  • Science and Technology Innovation Key R&D Program of Chongqing (CSTB2024TIAD-STX0024)
  • National Natural Science Foundation of China (62372072)
  • National Natural Science Foundation of China (52272388)
  • Natural Science Foundation of Chongqing, China (CSTB2025NSCQ-LZX0165)
  • Chongqing Talent Program Contract System Project (cstc2024ycjh-bgzxm0042)
  • China's National Key Program Project (2025YFE0103000)
  • Haihe Lab of Information Technology Application Innovation (22HHXCJC00002)
  • Sichuan Science and Technology Program (2024YFHZ0097)

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