English

A Demonstration of Over-the-Air Computation for Federated Edge Learning

Signal Processing 2022-09-22 v1 Artificial Intelligence Information Theory math.IT

Abstract

In this study, we propose a general-purpose synchronization method that allows a set of software-defined radios (SDRs) to transmit or receive any in-phase/quadrature data with precise timings while maintaining the baseband processing in the corresponding companion computers. The proposed method relies on the detection of a synchronization waveform in both receive and transmit directions and controlling the direct memory access blocks jointly with the processing system. By implementing this synchronization method on a set of low-cost SDRs, we demonstrate the performance of frequency-shift keying (FSK)-based majority vote (MV), i.e., an over-the-air computation scheme for federated edge learning, and introduce the corresponding procedures. Our experiment shows that the test accuracy can reach more than 95% for homogeneous and heterogeneous data distributions without using channel state information at the edge devices.

Keywords

Cite

@article{arxiv.2209.09954,
  title  = {A Demonstration of Over-the-Air Computation for Federated Edge Learning},
  author = {Alphan Sahin},
  journal= {arXiv preprint arXiv:2209.09954},
  year   = {2022}
}

Comments

9 Figures, 7 Pages. Accepted to GLOBECOM'2022 Workshops: Edge Learning over 5G Mobile Networks and Beyond