通过语义聚类提升多任务通信协作效率，避免负面迁移。
Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking
- 基于语义相似性分组任务，分阶段优化协作结构。
- 聚类后组内联合训练，较独立训练提升准确率。
- 适合需多任务协同的智能通信系统设计者。
协作式多任务语义通信（CMT-SemCom）通过共享表示提升任务执行性能。然而，如我们在[1]中所展示的，协作可能产生建设性或破坏性效果，取决于任务间的语义关系。为确保建设性协作，我们提出一种面向CMT-SemCom的语义感知任务聚类方法。该方法构建了一个分阶段的优化问题：在短时初始训练后，利用层次密度空间聚类对语义对齐的任务进行分组，随后仅在发现的组内开展端到端（E2E）联合训练。具体分为两个阶段：(i) 基于层次密度的空间聚类问题；(ii) 组内E2E CMT-SemCom学习问题。仿真结果表明，所提框架有效缓解了破坏性协作与负向迁移，相较于未聚类的多任务及独立训练基线，显著提升了准确率。
原文摘要 · Abstract (English)
Cooperative multi-task semantic communication (CMT-SemCom) improves task execution performance by leveraging shared representations. However, as we demonstrated in [1], cooperative multi-tasking can be either constructive or destructive, depending on the semantic relationships among tasks. To ensure constructive cooperation, we propose a semantic-aware task clustering method for CMT-SemCom. We have formulated a sequential multi-stage optimization problem in which semantically aligned tasks are clustered once after a short initial training phase, and then end-to-end (E2E) joint training is conducted exclusively within the discovered groups. Specifically, the problem decomposes into two stages: (i) a semantic clustering problem leveraging hierarchical density-based spatial clustering, and (ii) an intra-cluster E2E CMT-SemCom learning problem. Simulation results demonstrate that the proposed framework effectively mitigates destructive cooperation and negative transfer, yielding accuracy gains compared to unclustered multi-tasking and individual training baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。