Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication
Ahmad Halimi Razlighi, Pallavi Dhingra, Edgar Beck, Bho Matthiesen, and Armin Dekorsy

TL;DR
This paper introduces a federated learning framework for multi-task semantic communication in satellite networks, utilizing semantic-aware task clustering to enhance cooperative performance and mitigate negative transfer effects.
Contribution
It extends existing cooperative multi-task semantic communication to distributed settings with a novel semantic-aware clustering method based on information theory.
Findings
Improved task cooperation in satellite networks.
Performance gain over unclustered federated learning.
Effective mitigation of negative transfer among heterogeneous tasks.
Abstract
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…
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Taxonomy
TopicsAge of Information Optimization · IoT and Edge/Fog Computing · Privacy-Preserving Technologies in Data
