Task-oriented semantic communication (SemCom) prioritizes task execution over accurate symbol reconstruction and is well-suited to emerging intelligent applications. Cooperative multi-task SemCom (CMT-SemCom) further improves task execution performance. However, [1] demonstrates that cooperative multi-tasking can be either constructive or destructive. Moreover, the existing CMT-SemCom framework is not directly applicable to distributed multi-user scenarios, such as non-terrestrial satellite networks, where each satellite employs an individual semantic encoder. In this paper, we extend our earlier CMT-SemCom framework to distributed settings by proposing a federated learning (FL) based CMT-SemCom that enables cooperative multi-tasking across distributed users. Moreover, to address performance degradation caused by negative information transfer among heterogeneous tasks, we propose a semantic-aware task clustering method integrated in the FL process to ensure constructive cooperation based on an information-theoretic approach. Unlike common clustering methods that rely on high-dimensional data or feature space similarity, our proposed approach operates in the low-dimensional semantic domain to identify meaningful task relationships. Simulation results based on a LEO satellite network setup demonstrate the effectiveness of our approach and performance gain over unclustered FL and individual single-task SemCom.

翻译：面向任务的语义通信（SemCom）优先考虑任务执行而非精确符号重建，非常适合新兴智能应用。协同多任务语义通信（CMT-SemCom）进一步提升了任务执行性能。然而，[1]的研究表明，协同多任务处理既可能具有建设性也可能具有破坏性。此外，现有的CMT-SemCom框架无法直接应用于分布式多用户场景（如非地面卫星网络），其中每颗卫星采用独立的语义编码器。本文通过提出一种基于联邦学习（FL）的CMT-SemCom框架，将我们早期的CMT-SemCom框架扩展至分布式环境，实现了跨分布式用户的协同多任务处理。此外，为解决异构任务间负向信息传递导致的性能下降问题，我们提出一种集成于联邦学习流程中的语义感知任务聚类方法，基于信息论方法确保建设性协作。与依赖高维数据或特征空间相似性的常见聚类方法不同，本方法在低维语义域中运行以识别有意义的任务关联。基于低地球轨道卫星网络设置的仿真结果表明，本方法相较于未聚类的联邦学习及独立单任务语义通信具有显著效能优势。