Reconfigurable Intelligent Surface-Enabled Federated Learning for Power-Constrained Devices

Federated learning (FL) has recently emerged as a novel technique for training shared machine models in distributed fashion while preserving data privacy. However, the application of FL wireless networks poses unique challenge on mobile users (MUs)’ battery lifetime. In this letter, we aim to apply reconfigurable intelligent surface (RIS)-aided power transfer facilitate sustainable FL-based networks. Our objective is minimize total transmit participating MUs by jointly optimizing transmission time, control, and RIS’s phase shifts. Numerical results demonstrate that minimized satisfying requirements both minimum harvested energy rate.