  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.
