loading page

Reconfigurable Intelligent Surface-Enabled Federated Learning for Power-Constrained Devices
  • +1
  • Quang Nhat Le ,
  • Lina Bariah ,
  • Octavia A. Dobre ,
  • Sami Muhaidat
Quang Nhat Le
Author Profile
Lina Bariah
Khalifa University

Corresponding Author:[email protected]

Author Profile
Octavia A. Dobre
Author Profile
Sami Muhaidat
Author Profile

Abstract

Federated learning (FL) has recently emerged as a novel technique for training shared machine learning models in a distributed fashion while preserving data privacy. However, the application of FL in wireless networks poses a unique challenge on the mobile users (MUs)’ battery lifetime. In this paper, we aim to apply reconfigurable intelligent surface (RIS)-aided wireless power transfer to facilitate sustainable FL-based wireless networks. Our objective is to minimize the total transmit power of participating MUs by jointly optimizing the transmission time, power control, and the RIS’s phase shifts. Numerical results demonstrate that the total transmit power is minimized while satisfying the requirements of both minimum harvested energy and transmission data rate.
Nov 2022Published in IEEE Communications Letters volume 26 issue 11 on pages 2725-2729. 10.1109/LCOMM.2022.3199168