Federated-Learning-Assisted RIS Active and Passive Beamforming with ADMM for IoT Devices

Federated learning (FL) and reconfigurable intelligent surfaces (RIS) are pivotal technologies for future Internet of Things (IoT) networks, enhancing user privacy and system efficiency. However, realizing their full potential necessitates a cohesive and synergistic integration, challenging the traditional view of them as disparate components. This paper tackles the complex problem of maximizing energy efficiency (EE)—a critical yet under-explored metric insuch tightly coupled FL-RIS systems. We address this gap by formulating ajoint optimization problem that intrinsically links the FL process with physical layer resource allocation. Our framework maximizes the system’s global EE by concurrently designing the base station’s active beamforming and the RIS’s passive phase shifts,with an FL aggregation mechanism that is explicitly channel-aware and adaptive to the RIS-optimized wireless environment. This co-design ensures RIS actively facilitates FL by establishing robust communication, while FL intelligently leverages these improved channels for efficient and accelerated learning, all under practical FL performance constraints. Simulation results demonstrate that our proposed framework significantly enhances system energy efficiency compared to several benchmark schemes and exhibits robust convergence properties.