In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-task SemCom focused on full observation settings, our research explores a more realistic case where only distributed partial observations are available, such as in a production line monitored by multiple sensing nodes. To address this, we propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side. We have used an information-theoretic perspective with variational approximations for our end-to-end data-driven approach. Simulation results demonstrate that the proposed cooperative and collaborative multi-task (CCMT) SemCom system significantly improves task execution accuracy, particularly in complex datasets, if the noise introduced from the communication channel is not limiting the task performance too much. Our findings contribute to a more general SemCom framework capable of handling distributed sources and multiple tasks simultaneously, advancing the applicability of SemCom systems in real-world scenarios.

翻译：本文探索了一种面向分布式源的多任务语义通信系统，将现有研究重点从协作式单任务执行扩展至多任务场景。我们基于[1]中提出的协作式多任务处理框架，该框架将编码器划分为公共单元和多个特定单元。尽管早期多任务语义通信研究集中于完全观测场景，但本研究探讨了一种更现实的场景，即仅能获取分布式部分观测数据，例如由多个传感节点监控的生产线。为此，我们提出一种语义通信系统，该系统通过发射端的分裂结构实现协同处理，并在接收端通过协作机制支持多任务处理。我们采用信息论视角，结合变分近似方法构建端到端数据驱动方案。仿真结果表明，若通信信道引入的噪声未过度限制任务性能，所提出的协同协作多任务语义通信系统能显著提升任务执行准确度，尤其在复杂数据集上表现突出。本研究成果有助于构建更通用的语义通信框架，使其能够同时处理分布式源与多任务，推动语义通信系统在实际场景中的应用。