Task-oriented semantic communication systems have emerged as a promising approach to achieving efficient and intelligent data transmission in next-generation networks, where only information relevant to a specific task is communicated. This is particularly important in 6G-enabled Internet of Things (6G-IoT) scenarios, where bandwidth constraints, latency requirements, and data privacy are critical. However, existing methods struggle to fully disentangle task-relevant and task-irrelevant information, leading to privacy concerns and suboptimal performance. To address this, we propose an information-bottleneck inspired method, named CLAD (contrastive learning and adversarial disentanglement). CLAD utilizes contrastive learning to effectively capture task-relevant features while employing adversarial disentanglement to discard task-irrelevant information. Additionally, due to the absence of reliable and reproducible methods to quantify the minimality of encoded feature vectors, we introduce the Information Retention Index (IRI), a comparative metric used as a proxy for the mutual information between the encoded features and the input. The IRI reflects how minimal and informative the representation is, making it highly relevant for privacy-preserving and bandwidth-efficient 6G-IoT systems. Extensive experiments demonstrate that CLAD outperforms state-of-the-art baselines in terms of semantic extraction, task performance, privacy preservation, and IRI, making it a promising building block for responsible, efficient and trustworthy 6G-IoT services.

翻译：任务导向语义通信系统已成为下一代网络中实现高效智能数据传输的一种有前景的方法，其仅传输与特定任务相关的信息。这在6G赋能的物联网（6G-IoT）场景中尤为重要，因为带宽限制、延迟要求和数据隐私至关重要。然而，现有方法难以完全解耦任务相关与任务无关信息，导致隐私隐患和次优性能。为解决此问题，我们提出一种受信息瓶颈启发的CLAD方法（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量化编码特征向量的最小性，我们引入了信息保留指数（IRI）作为比较度量指标，用以近似表征编码特征与输入之间的互信息。IRI反映了表征的极小性与信息量，使其与隐私保护和带宽高效的6G-IoT系统高度相关。大量实验表明，CLAD在语义提取、任务性能、隐私保护和IRI指标方面均优于现有基线方法，有望成为构建负责任、高效且可信的6G-IoT服务的关键组件。