Region-specific reliable channel estimation in RIS-enabled wireless communications via clustered federated learning

Abstract

Machine learning (ML)-based downlink channel estimation for reconfigurable intelligent surface (RIS)-assisted communication faces challenges such as handling channel variations, high communication overhead of centralized learning (CL), and vulnerability to malicious users. We propose a novel approach integrating blockchain to enhance security by verifying registered users, autoencoder (AE)-based clustering to identify regions within the cell, and clustered federated learning (CFL) to ensure good channel estimation performance while minimizing communication and energy overhead. Simulations show that the proposed clustering-based scheme achieves estimation performance comparable to CL while significantly reducing communication and energy overhead.