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

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.

Machine Learning (cs.LG); Information Theory (cs.IT); Signal Processing (eess.SP); Machine Learning (stat.ML)

arXiv:2607.21426 [cs.LG]

(or arXiv:2607.21426v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.21426 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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