Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking

2026-07-23

AI Summary

In multi-task semantic communication, improper modeling of inter-task semantic relationships often leads to negative transfer and destructive collaboration, degrading overall performance. To address this issue, this work introduces semantic relationship modeling into multi-task clustering for the first time and proposes a semantic-aware two-stage optimization framework. The approach first groups tasks via hierarchical density-based clustering to achieve semantic alignment, followed by end-to-end joint training within each cluster. This strategy effectively fosters constructive collaboration among related tasks, significantly improving accuracy and substantially mitigating negative transfer compared to both unclustered multi-task learning and independently trained baselines.

Abstract

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.

Problem

• cooperative multi-tasking
• semantic communication
• destructive cooperation
• negative transfer
• task clustering

Innovation

• semantic-aware clustering
• cooperative multi-task learning
• constructive cooperation
• hierarchical density-based clustering
• end-to-end semantic communication