Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication

2026-01-24

AI Summary

This work addresses the challenge of negative information transfer in cooperative multi-task semantic communication within distributed multi-user scenarios, where task heterogeneity often degrades performance. To mitigate this issue, the authors integrate semantic communication into a federated learning framework and propose an information-theoretic, semantics-aware task clustering method. By identifying task affinities in a low-dimensional semantic space, the approach avoids reliance on high-dimensional feature representations and facilitates positive knowledge transfer across related tasks. The effectiveness of the proposed method is validated through experiments conducted in a simulated low Earth orbit satellite network environment, demonstrating significant improvements in multi-task performance compared to both non-clustered federated learning and single-task semantic communication baselines.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Multiagent Systems: Agent Communication

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 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.

Problem

• Federated Learning
• Semantic Communication
• Multi-Task Learning
• Task Clustering
• Information Transfer

Innovation

• Federated Learning
• Semantic Communication
• Task Clustering
• Multi-Task Learning
• Information-Theoretic Approach