In this work, we present an energy-efficient distributed learning framework using low-resolution ADCs and coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware least-mean square (DQA-LMS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. We also carry out a statistical analysis of the proposed DQA-LMS algorithm that includes a stability condition. Simulations assess the DQA-LMS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode and demonstrate the effectiveness of the DQA-LMS algorithm.
@article{arxiv.2101.04824,
title = {Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals},
author = {A. Danaee and R. C. de Lamare and V. H. Nascimento},
journal= {arXiv preprint arXiv:2101.04824},
year = {2021}
}
Comments
5 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2012.10939