In this work, we present an energy-efficient distributed learning framework using coarsely quantized signals for Internet of Things (IoT) networks. In particular, we develop a distributed quantization-aware recursive least squares (DQA-RLS) algorithm that can learn parameters in an energy-efficient fashion using signals quantized with few bits while requiring a low computational cost. Numerical results assess the DQA-RLS algorithm against existing techniques for a distributed parameter estimation task where IoT devices operate in a peer-to-peer mode.
@article{arxiv.2012.10939,
title = {Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals},
author = {A. Danaee and R. C. de Lamare and V. H. Nascimento},
journal= {arXiv preprint arXiv:2012.10939},
year = {2020}
}