  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.
