Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission, where only information relevant to a specific task is communicated. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and subpar performance. To address this, we propose an information-bottleneck method, named CLAD (contrastive learning and adversarial disentanglement). CLAD leverages contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the lack of reliable and reproducible methods to gain insight into the informativeness and minimality of the encoded feature vectors, we introduce a new technique to compute the information retention index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input, reflecting the minimality of the encoded features. The IRI quantifies the minimality and informativeness of the encoded feature vectors across different task-oriented communication techniques. Our extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of task performance, privacy preservation, and IRI. CLAD achieves a predictive performance improvement of around 2.5-3%, along with a 77-90% reduction in IRI and a 57-76% decrease in adversarial accuracy.

翻译：任务导向语义通信系统已成为实现高效智能数据传输的一种前景广阔的方法，其仅传输与特定任务相关的信息。然而，现有方法难以完全解耦任务相关与任务无关信息，导致隐私隐患与性能欠佳。为解决此问题，我们提出一种基于信息瓶颈的方法，命名为CLAD（对比学习与对抗解耦）。CLAD利用对比学习有效捕获任务相关特征，同时采用对抗解耦机制剔除任务无关信息。此外，由于缺乏可靠且可复现的方法来深入理解编码特征向量的信息量与最小性，我们引入一种新技术来计算信息保留指数（IRI）。该指数作为编码特征与输入之间互信息的代理比较指标，反映了编码特征的最小性。IRI可量化不同任务导向通信技术中编码特征向量的最小性与信息量。大量实验表明，CLAD在任务性能、隐私保护及IRI指标上均优于现有先进基线方法。CLAD实现了约2.5-3%的预测性能提升，同时使IRI降低77-90%，对抗性准确率下降57-76%。