Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication
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arXiv
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| Format: | Preprint |
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2026
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| author | Razlighi, Ahmad Halimi Dhingra, Pallavi Beck, Edgar Matthiesen, Bho Dekorsy, Armin |
| author_facet | Razlighi, Ahmad Halimi Dhingra, Pallavi Beck, Edgar Matthiesen, Bho Dekorsy, Armin |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_17419 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication Razlighi, Ahmad Halimi Dhingra, Pallavi Beck, Edgar Matthiesen, Bho Dekorsy, Armin Signal Processing Information Theory Machine Learning 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. |
| title | Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication |
| topic | Signal Processing Information Theory Machine Learning |
| url | https://arxiv.org/abs/2601.17419 |