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arXiv · 2012.10939

Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals

A. Danaee·R. C. de Lamare·V. H. Nascimento
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Abstract

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.

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A. Danaee, R. C. de Lamare, V. H. Nascimento. 2020-12-20. Study of Energy-Efficient Distributed RLS-based Learning with Coarsely Quantized Signals. https://arxiv.org/abs/2012.10939

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