Energy-Efficient Distributed Learning Algorithms for Coarsely Quantized Signals

TL;DR

This paper introduces an energy-efficient distributed learning algorithm tailored for IoT networks using low-resolution quantization, enabling low-cost and power-efficient parameter estimation.

Contribution

The paper proposes a novel distributed quantization-aware LMS algorithm designed for coarsely quantized signals in IoT networks, with stability analysis and simulation validation.

Findings

DQA-LMS outperforms existing methods in energy efficiency.

The algorithm maintains stability under certain conditions.

Simulation results confirm effective parameter estimation.

Abstract

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.