用联邦强化学习提升下一代网络的物理层安全
Federated Learning-based MARL for Strengthening Physical-Layer Security in B5G Networks
- 各基站作为智能体，通过联邦学习共享参数而非数据
- RDPG算法收敛更快，安全速率优于传统分布式方法
- 适合关注无线网络安全与隐私保护的研究者
本文研究了基于联邦学习的多智能体强化学习（MARL）在超越5G（B5G）多小区网络中增强物理层安全（PLS）的应用。每个小区的基站（BS）作为深度强化学习（DRL）智能体，与环境交互以最大化合法用户的保密速率，对抗窃听者对基站与授权用户间通信的窃取。这些DRL智能体采用联邦模式，仅向中央服务器共享网络参数，不共享合法用户的私有数据。本文对比了两种DRL方法：深度Q网络（DQN）和Reinforce深度策略梯度（RDPG），结果表明RDPG收敛速度更快。此外，所提方法优于分布式DRL方案。同时，实验揭示了安全性与复杂性之间的权衡关系。
原文摘要 · Abstract (English)
This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.
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