Task-oriented communication aims to extract and transmit task-relevant information to significantly reduce the communication overhead and transmission latency. However, the unpredictable distribution shifts between training and test data, including domain shift and semantic shift, can dramatically undermine the system performance. In order to tackle these challenges, it is crucial to ensure that the encoded features can generalize to domain-shifted data and detect semanticshifted data, while remaining compact for transmission. In this paper, we propose a novel approach based on the information bottleneck (IB) principle and invariant risk minimization (IRM) framework. The proposed method aims to extract compact and informative features that possess high capability for effective domain-shift generalization and accurate semantic-shift detection without any knowledge of the test data during training. Specifically, we propose an invariant feature encoding approach based on the IB principle and IRM framework for domainshift generalization, which aims to find the causal relationship between the input data and task result by minimizing the complexity and domain dependence of the encoded feature. Furthermore, we enhance the task-oriented communication with the label-dependent feature encoding approach for semanticshift detection which achieves joint gains in IB optimization and detection performance. To avoid the intractable computation of the IB-based objective, we leverage variational approximation to derive a tractable upper bound for optimization. Extensive simulation results on image classification tasks demonstrate that the proposed scheme outperforms state-of-the-art approaches and achieves a better rate-distortion tradeoff.

翻译：任务导向通信旨在提取并传输与任务相关的信息，以显著降低通信开销和传输延迟。然而，训练数据与测试数据之间不可预测的分布偏移（包括领域偏移和语义偏移）可能会严重破坏系统性能。为解决这些挑战，确保编码特征能够泛化到领域偏移数据并检测语义偏移数据，同时保持紧凑性以实现高效传输至关重要。本文提出一种基于信息瓶颈（IB）原理和不变风险最小化（IRM）框架的新方法。该方法旨在提取紧凑且信息丰富的特征，这些特征具备高效领域偏移泛化能力和准确语义偏移检测能力，且在训练过程中无需任何测试数据信息。具体而言，我们提出基于IB原理和IRM框架的不变特征编码方法用于领域偏移泛化，通过最小化编码特征的复杂性和领域依赖性，寻找输入数据与任务结果之间的因果关系。此外，我们采用基于标签依赖特征编码的方法增强任务导向通信以实现语义偏移检测，该方法在IB优化和检测性能方面实现联合增益。为避免基于IB目标的难以处理的计算，我们利用变分近似推导出一个可处理的上界以进行优化。在图像分类任务上的大量仿真结果表明，所提方案优于现有最新方法，并实现了更优的率失真权衡。