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

Quang Nhat Le, Lina Bariah, Octavia A. Dobre, Sami Muhaidat

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 letter, 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.

Original languageBritish English
Pages (from-to)2725-2729
Number of pages5
JournalIEEE Communications Letters
Volume26
Issue number11
DOIs
StatePublished - 1 Nov 2022

Keywords

  • Energy harvesting
  • federated learning
  • optimization
  • reconfigurable intelligent surface

Fingerprint

Dive into the research topics of 'Reconfigurable Intelligent Surface-Enabled Federated Learning for Power-Constrained Devices'. Together they form a unique fingerprint.

Cite this