Semantic communication shifts the focus from bit-level accuracy to task-relevant semantic delivery, enabling efficient and intelligent communication for next-generation networks. However, existing multi-modal solutions often process all available data modalities indiscriminately, ignoring that their contributions to downstream tasks are often unequal. This not only leads to severe resource inefficiency but also degrades task inference performance due to irrelevant or redundant information. To tackle this issue, we propose a novel task-aware distributed information bottleneck (TADIB) framework, which quantifies the contribution of any set of modalities to given tasks. Based on this theoretical framework, we design a practical coding scheme that intelligently selects and compresses only the most task-relevant modalities at the transmitter. To find the optimal selection and the codecs in the network, we adopt the probabilistic relaxation of discrete selection, enabling distributed encoders to make coordinated decisions with score function estimation and common randomness. Extensive experiments on public datasets demonstrate that our solution matches or surpasses the inference quality of full-modal baselines while significantly reducing communication and computational costs.

翻译：语义通信将关注点从比特级精度转向任务相关语义的传递，为下一代网络实现高效智能通信。然而，现有多模态解决方案往往不加区分地处理所有可用数据模态，忽略了不同模态对下游任务的贡献通常并不均衡。这不仅导致严重的资源低效，还会因无关或冗余信息而降低任务推理性能。为解决此问题，我们提出一种新颖的任务感知分布式信息瓶颈框架，该框架能够量化任意模态集合对给定任务的贡献度。基于此理论框架，我们设计了一种实用的编码方案，在发送端智能地选择并仅压缩最具任务相关性的模态。为寻找网络中最优的模态选择与编解码器配置，我们采用离散选择的概率松弛方法，使分布式编码器能够通过得分函数估计与公共随机性实现协同决策。在公开数据集上的大量实验表明，我们的解决方案在显著降低通信与计算成本的同时，达到甚至超越了全模态基线的推理质量。