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

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.