# A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT

arXiv: https://arxiv.org/abs/2203.09631 · HTML: https://mlsanity.com/paper/2203.09631/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [Minimum Energy Quantized Neural Networks](https://mlsanity.com/paper/1711.00215/) (2017)
- [Blind Denoising Autoencoder](https://mlsanity.com/paper/1912.07358/) (2019)
- [PRVNet: A Novel Partially-Regularized Variational Autoencoders for Massive MIMO CSI Feedback](https://mlsanity.com/paper/2011.04178/) (2020)
- [On Quantizer Design for Distributed Bayesian Estimation in Sensor Networks](https://mlsanity.com/paper/1407.7152/) (2014)

## Built on by (most cited first, 1 of 1)

- [Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey](https://mlsanity.com/paper/2302.13876/) (2023)

## Abstract (arXiv)

In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmitting them to a fusion center (FC) where a global decision is inferred. Most of the existing compression techniques and entropy quantizers consider only the reconstruction fidelity as a metric, which means they decouple the compression from the sensing goal. In this work, we argue that data compression mechanisms and entropy quantizers should be co-designed with the sensing goal, specifically for machine-consumed data. To this end, we propose a novel deep learning-based framework for compressing and quantizing the observations of correlated sensors. Instead of maximizing the reconstruction fidelity, our objective is to compress the sensor observations in a way that maximizes the accuracy of the inferred decision (i.e., sensing goal) at the FC. Unlike prior work, we do not impose any assumptions about the observations distribution which emphasizes the wide applicability of our framework. We also propose a novel loss function that keeps the model focused on learning complementary features at each sensor. The results show the superior performance of our framework compared to other benchmark models.