As wireless systems evolve toward Beyond 5G (B5G), the adoption of cell-free (CF) millimeter-wave (mmWave) architectures combined with Reconfigurable Intelligent Surfaces (RIS) is emerging as a key enabler for ultra-reliable, high-capacity, scalable, and secure Industrial Internet of Things (IIoT) communications. However, safeguarding these complex and distributed environments against eavesdropping remains a critical challenge, particularly when conventional security mechanisms struggle to overcome scalability, and latency constraints. In this paper, a novel framework for detecting malicious users in RIS-enhanced cell-free mmWave networks using Federated Learning (FL) is presented. The envisioned setup features multiple access points (APs) operating without traditional cell boundaries, assisted by RIS nodes to dynamically shape the wireless propagation environment. Edge devices collaboratively train a Deep Convolutional Neural Network (DCNN) on locally observed Channel State Information (CSI), eliminating the need for raw data exchange. Moreover, an early-exit mechanism is incorporated in that model to jointly satisfy computational complexity requirements. Performance evaluation indicates that the integration of FL and multi-RIS coordination improves approximately 30% the achieved secrecy rate (SR) compared to baseline non-RIS-assisted methods while maintaining near-optimal detection accuracy levels. This work establishes a distributed, privacy-preserving approach to physical layer eavesdropping detection tailored for next-generation IIoT deployments.

翻译：随着无线系统向超5G（B5G）演进，无蜂窝毫米波架构与可重构智能超表面（RIS）的结合，正成为实现超可靠、高容量、可扩展且安全的工业物联网（IIoT）通信的关键使能技术。然而，在这种复杂分布式环境中防御窃听攻击仍是严峻挑战，尤其在传统安全机制难以克服可扩展性与时延限制的情况下。本文提出一种基于联邦学习（FL）的新型框架，用于在RIS增强的无蜂窝毫米波网络中检测恶意用户。该架构采用多个在无传统小区边界条件下工作的接入点（AP），并借助RIS节点动态调控无线传播环境。边缘设备基于本地观测的信道状态信息（CSI）协同训练深度卷积神经网络（DCNN），无需原始数据交换。此外，模型中引入了早退机制以共同满足计算复杂度约束。性能评估表明，相较于未采用RIS辅助的基线方法，FL与多RIS协同的集成方案将保密速率（SR）提升约30%，同时保持接近最优的检测准确率。本研究为面向下一代IIoT部署的物理层窃听检测，建立了一种分布式、隐私保护的解决方案。