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

arXiv: https://arxiv.org/abs/2603.10977 · HTML: https://mlsanity.com/paper/2603.10977/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Cell-Free Massive MIMO for 6G Wireless Communication Networks](https://mlsanity.com/paper/2110.07309/) (2021)
- [Reconfigurable Intelligent Surface for Physical Layer Security in 6G-IoT: Designs, Issues, and Advances](https://mlsanity.com/paper/2311.08112/) (2023)
- [Ultra-Dense Cell-Free Massive MIMO for 6G: Technical Overview and Open Questions](https://mlsanity.com/paper/2401.03898/) (2024)
- [CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming](https://mlsanity.com/paper/2403.10873/) (2024)
- [RIS-Aided Cell-Free Massive MIMO Systems for 6G: Fundamentals, System Design, and Applications](https://mlsanity.com/paper/2310.00263/) (2023)
- [ML-Enabled Eavesdropper Detection in Beyond 5G IIoT Networks](https://mlsanity.com/paper/2505.07837/) (2025)

## Abstract (arXiv)

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.