Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach

By Jiawei Shao, Yuyi Mao, Jun Zhang. IEEE Journal on Selected Areas in Communications, 2021

359 citations recorded by the source corpus.

This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an informative and compact representation for low-latency inference given the limited bandwidth. We propose a learning-based communication scheme that jointly optimizes feature extraction, source coding, and channel coding in a task-oriented manner, i.e., targeting the downstream inference task rather than data reconstruction. Specifically, we leverage an information bottleneck (IB) framework to formalize a rate-distortion tradeoff between the informativeness of the encoded feature and the inference performance. As the IB optimization is computationally prohibitive for the high-dimensional data, we adopt a variational approximation, namely the variational information bottleneck (VIB), to build a tractable upper bound. To reduce the communication overhead, we leverage a sparsity-inducing distribution as the variational prior for the VIB framework to sparsify the encoded feature vector. Furthermore, considering dynamic channel conditions in practical communication systems, we propose a variable-length feature encoding scheme based on dynamic neural networks to adaptively adjust the activated dimensions of the encoded feature to different channel conditions. Extensive experiments evidence that the proposed task-oriented communication system achieves a better rate-distortion tradeoff than baseline methods and significantly reduces the feature transmission latency in dynamic channel conditions.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/17858a252b041b4d7af8e87b876392f198a3e273

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Citations in the corpus

Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, positive): “optimization, this method in [121] was then extended in [227] to deal with the same task while simultaneously minimizing energy consumption and service latency.”
Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications (2022, neutral): “A semantic communication system for image classification has been proposed in [255], which uses a variational IB (VIB) framework to overcome the difficulty in mutual information computation of the original IB [89].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “Recently, a novel and generic design framework for task-oriented communication was developed in [20], which is based on the information bottleneck formulation [235].”