FRIEND: Federated Learning for Joint Optimization of multi-RIS Configuration and Eavesdropper Intelligent Detection in B5G Networks

2026-03-11

AI Summary

This work addresses the challenge of achieving scalability, low latency, and robustness against eavesdropping in beyond-5G cell-free millimeter-wave networks, where conventional security mechanisms fall short. To this end, the authors propose a novel framework that synergistically integrates federated learning with multiple reconfigurable intelligent surfaces (RISs). Edge devices collaboratively train a deep convolutional neural network using only local channel state information—without sharing raw data—to intelligently detect eavesdroppers and jointly optimize RIS configurations. An innovative early-exit mechanism is introduced to enhance computational efficiency while preserving data privacy. Experimental results demonstrate that the proposed approach achieves approximately a 30% improvement in secrecy rate over non-RIS baseline schemes, while maintaining near-optimal eavesdropper detection accuracy.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

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.

Problem

• Federated Learning
• Reconfigurable Intelligent Surfaces
• Eavesdropper Detection
• Cell-free mmWave Networks
• Physical Layer Security

Innovation

• Federated Learning
• Reconfigurable Intelligent Surface
• Eavesdropper Detection
• Cell-Free mmWave
• Early-Exit Mechanism