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 an information bottleneck (IB) principle to extract the task-relevant feature at each edge device and adopt a distributed information bottleneck (DIB) framework 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 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 of multiple edge devices to attain additional communication overhead reduction. Extensive experiments evidence that the proposed task-oriented communication scheme achieves a better rate-relevance tradeoff than baseline methods.

翻译：本文研究面向多设备协作边缘推理的任务导向通信问题，其中一组分布式低端边缘设备将本地样本的提取特征传输至高性能边缘服务器进行推理。虽然协作边缘推理能够克服单设备感知能力的局限性，但会显著增加通信开销并可能导致过高延迟。为实现低延迟协作推理，我们提出一种基于学习的通信方案，该方案以任务导向方式优化局部特征提取与分布式特征编码，即消除数据冗余并传输对下游推理任务至关重要的信息，而非在边缘服务器重建原始数据样本。具体而言，我们利用信息瓶颈（IB）原理提取每个边缘设备的任务相关特征，并采用分布式信息瓶颈（DIB）框架形式化表征分布式特征编码的最优速率-相关性权衡的单字母刻画。为灵活控制通信开销，我们将DIB框架扩展为分布式确定性信息瓶颈（DDIB）目标函数，显式纳入编码特征的表示成本。由于基于IB的目标函数对高维数据计算代价过高，我们采用变分近似方法使优化问题易于处理。为补偿变分近似可能导致的性能损失，我们进一步开发选择性重传（SR）机制，用于识别多个边缘设备编码特征的冗余性，从而进一步降低通信开销。大量实验证明，所提出的任务导向通信方案在速率-相关性权衡方面优于基线方法。