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Task-aware Distributed Source Coding under Dynamic Bandwidth

Po-han Li, Sravan Kumar Ankireddy, Ruihan Zhao, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari, Ufuk Topcu, Sandeep Chinchali, Hyeji Kim

TL;DR

This work addresses how to compress data from multiple correlated sensors under dynamic uplink bandwidth to maximize downstream task performance. It introduces NDPCA, a framework that combines neural autoencoders with a distributed PCA module to learn task-relevant, low-rank representations and allocate bandwidth adaptively with a single model. The authors provide a theoretical DPCA analysis for linear encoders and demonstrate empirical gains on CIFAR-10 denoising, multi-view robotic manipulation, and satellite object detection, including PSNR, success rate, and mAP improvements. The results show that task-aware, bandwidth-aware compression can achieve graceful performance–bandwidth trade-offs without retraining for different bandwidths and pave the way for robust, scalable multi-sensor inference.

Abstract

Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node due to limited communication bandwidth. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based task to generate the final output. Thus, it is important to compress the features that are relevant to the task. Additionally, the final performance depends heavily on the total available bandwidth. In practice, it is common to encounter varying availability in bandwidth, and higher bandwidth results in better performance of the task. We design a novel distributed compression framework composed of independent encoders and a joint decoder, which we call neural distributed principal component analysis (NDPCA). NDPCA flexibly compresses data from multiple sources to any available bandwidth with a single model, reducing computing and storage overhead. NDPCA achieves this by learning low-rank task representations and efficiently distributing bandwidth among sensors, thus providing a graceful trade-off between performance and bandwidth. Experiments show that NDPCA improves the success rate of multi-view robotic arm manipulation by 9% and the accuracy of object detection tasks on satellite imagery by 14% compared to an autoencoder with uniform bandwidth allocation.

Task-aware Distributed Source Coding under Dynamic Bandwidth

TL;DR

This work addresses how to compress data from multiple correlated sensors under dynamic uplink bandwidth to maximize downstream task performance. It introduces NDPCA, a framework that combines neural autoencoders with a distributed PCA module to learn task-relevant, low-rank representations and allocate bandwidth adaptively with a single model. The authors provide a theoretical DPCA analysis for linear encoders and demonstrate empirical gains on CIFAR-10 denoising, multi-view robotic manipulation, and satellite object detection, including PSNR, success rate, and mAP improvements. The results show that task-aware, bandwidth-aware compression can achieve graceful performance–bandwidth trade-offs without retraining for different bandwidths and pave the way for robust, scalable multi-sensor inference.

Abstract

Efficient compression of correlated data is essential to minimize communication overload in multi-sensor networks. In such networks, each sensor independently compresses the data and transmits them to a central node due to limited communication bandwidth. A decoder at the central node decompresses and passes the data to a pre-trained machine learning-based task to generate the final output. Thus, it is important to compress the features that are relevant to the task. Additionally, the final performance depends heavily on the total available bandwidth. In practice, it is common to encounter varying availability in bandwidth, and higher bandwidth results in better performance of the task. We design a novel distributed compression framework composed of independent encoders and a joint decoder, which we call neural distributed principal component analysis (NDPCA). NDPCA flexibly compresses data from multiple sources to any available bandwidth with a single model, reducing computing and storage overhead. NDPCA achieves this by learning low-rank task representations and efficiently distributing bandwidth among sensors, thus providing a graceful trade-off between performance and bandwidth. Experiments show that NDPCA improves the success rate of multi-view robotic arm manipulation by 9% and the accuracy of object detection tasks on satellite imagery by 14% compared to an autoencoder with uniform bandwidth allocation.
Paper Structure (27 sections, 3 theorems, 28 equations, 12 figures, 1 table, 1 algorithm)

This paper contains 27 sections, 3 theorems, 28 equations, 12 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Theoretical Analysis
  4. Neural Distributed Principal Component Analysis
  5. Experiments
  6. Related Work
  7. Conclusion and Future Work
  8. Proofs of Lemmas
  9. Bounds of DPCA
  10. Why Robust Task?
  11. The Gaussian CEO Problem
  12. NDPCA with $4$ sources:
  13. Details of the Datasets
  14. CIFAR-10 denoising:
  15. Locate and lift:
  16. ...and 12 more sections

Key Result

Lemma 3.1

Given a zero-mean data matrix and its covariance, assume that $\Delta X$ is relatively smaller than $XX^\top$, and $XX^\top$ is positive definite with distinct eigenvalues. For PCA's encoding and decoding matrices $E_{\mathrm{PCA}}, D_{\mathrm{PCA}}$ and DPCA's encoding and decoding matrices $E_{\mathrm{DPCA}}, D_{\mathrm{DPCA}}$, the difference of where $\lambda_i$ and $e_i$ are the $i$-th large

Figures (12)

  • Figure 1: Task-aware distributed source coding with NDPCA.$X_{1}, \dots, X_{k}$ are correlated data sources. Encoders $E_{1}, \dots, E_{k}$ independently compress data to latent representations $Z_{1}, \dots, Z_{k}$. Using linear matrices, the DPCA module projects the representations to any lower dimension at the encoder and projects them back to the original data space at the decoder, which allocates the bandwidth of sources based on the importance of the task $\Phi$. The goal is to find the optimal encoders and decoder that minimize the final task loss.
  • Figure 2: Datasets: (column $1$) view $1$. (column $2$) view $2$. In all experiments, both views are correlated, but one view is more important than the other as it contains more information relevant to the task.
  • Figure 3: Top: Performance Comparison for 3 different tasks. Our method achieves equal or higher performance than other methods. Bottom: Distribution of total available bandwidth (latent space) among the two views for NDPCA (ours). The unequal allocation highlights the difference in the importance of the views for a given task.
  • Figure 4: Task-aware v.s. task-agnostic: Ground-truth bounding boxes are red (row $1$), while detected boxes of task-aware are yellow (row $2$). Nothing is detected in the task-agnostic setting (row $3$). Task-agnostic images are perceptible to human eyes, while task-aware images capture task-relevant features, thus imperceptible to human eyes.
  • Figure 5: Bound from Lemma \ref{['lemma:boundDPCA']}: The obtained upper bound is always larger than the difference of losses of DPCA and PCA.
  • ...and 7 more figures

Theorems & Definitions (5)

  • Lemma 3.1: Bounds of DPCA Reconstruction
  • Lemma : Bounds of DPCA Reconstruction
  • proof
  • Lemma A.1: Why task-aware compression and a robust task
  • proof