Task-Oriented Communication for Multidevice Cooperative Edge Inference

• Published in: IEEE transactions on wireless communications 2023-01, Vol.22 (1), p.73-87
• Main Authors: Shao, Jiawei, Mao, Yuyi, Zhang, Jun
• Format: Article
• Language: English
• Subjects: Approximation; Coding; Communication; Distributed databases; distributed information bottleneck (DIB); Encoding; Feature extraction; Image classification; Image edge detection; Inference; information bottleneck (IB); Object recognition; Optimization; Performance evaluation; Redundancy; Servers; Task analysis; Task-oriented communication; Tradeoffs; variational inference
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2022.3191118

This paper investigates task-oriented communication for multi-device cooperative edge inference, where a group of distributed low-end edge devices transmit the extracted features of local samples to a powerful edge server for inference. While cooperative edge inference can overcome the limited sensing capability of a single device, it substantially increases the communication overhead and may incur excessive latency. To enable low-latency cooperative inference, we propose a learning-based communication scheme that optimizes local feature extraction and distributed feature encoding in a task-oriented manner, i.e., to remove data redundancy and transmit information that is essential for the downstream inference task rather than reconstructing the data samples at the edge server. Specifically, we leverage Tishby's information bottleneck (IB) principle (Tishby et al., 2000) to extract the task-relevant feature at each edge device, and adopt the distributed information bottleneck (DIB) framework of Aguerri and Zaidi, 2021, to formalize a single-letter characterization of the optimal rate-relevance tradeoff for distributed feature encoding. To admit flexible control of the communication overhead, we extend the DIB framework to a distributed deterministic information bottleneck (DDIB) objective that explicitly incorporates the representational costs of the encoded features. As the IB-based objectives are computationally prohibitive for high-dimensional data, we adopt variational approximations to make the optimization problems tractable. To compensate for the potential performance loss due to the variational approximations, we also develop a selective retransmission (SR) mechanism to identify the redundancy in the encoded features among multiple edge devices to attain additional communication overhead reduction. Extensive experiments on multi-view image classification and multi-view object recognition tasks evidence that the proposed task-oriented communication scheme achieves a better rate-relevance tradeoff than existing methods.