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

• URL: http://arxiv.org/abs/2601.17419v1
• Date: Sat, 24 Jan 2026 11:41:00 GMT
• Title: Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication
• Abstract summary: Task-oriented semantic communication (SemCom) prioritizes task execution over accurate symbol reconstruction.<n>We extend our CMT-SemCom framework to distributed settings by proposing a federated learning (FL) based CMT-SemCom.<n>We propose a semantic-aware task clustering method integrated in the FL process to ensure constructive cooperation.
• Score: 6.198237241838558
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Cooperative Multi-Task Semantic Communication for Joint Classification and Regression Tasks [4.610618579893945]
  MultiTask semantic communication (SemCom) prioritizes simultaneous execution of multiple tasks over bit-accurate reconstruction in future networks.<n>CMT-SemCom has been evaluated on homogeneous classification tasks on simplistic datasets.<n>We extend our CMT-SemCom to jointly handle heterogeneous classification and regression tasks on the complex City dataset.
  arXiv  Detail & Related papers  (2026-09-03T15:13:25Z)
• Semantic-Aware Task Clustering for Constructive and Cooperative Multi-Tasking [6.198237241838558]
  We propose a semantic-aware task clustering method for CMT-SemCom.<n>We show that the proposed framework effectively mitigates destructive cooperation and negative transfer.
  arXiv  Detail & Related papers  (2026-07-23T15:29:39Z)
• Robust multi-task boosting using clustering and local ensembling [6.920276126310231]
  We propose Robust Multi-Task Boosting using Clustering and Local Ensembling (RMB-CLE)<n>RMB-CLE derives inter-task similarity directly from cross-task errors.<n>Within each cluster, a local ensemble enables robust knowledge sharing.
  arXiv  Detail & Related papers  (2026-02-15T16:59:23Z)
• TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent System [13.255369400663893]
  TodyComm is a textbftask-textbforiented textbfdynamic textbfcommunication algorithm.<n>It produces behavior-driven collaboration topologies that adapt to the dynamics at each round, optimizing the utility for the task through policy gradient.
  arXiv  Detail & Related papers  (2026-02-03T16:07:59Z)
• MEJO: MLLM-Engaged Surgical Triplet Recognition via Inter- and Intra-Task Joint Optimization [52.149337961205624]
  We propose a framework that empowers both inter- and intra-task optimization for surgical triplet recognition.<n>For inter-task optimization, we introduce the Shared-Specific-Disentangled (S$2$D) learning scheme that decomposes representations into task-shared and task-specific components.<n>For intra-task optimization conflicts, we develop a Coordinated Gradient Learning (CGL) strategy, which dissects and rebalances the positive-negative ambiguities.
  arXiv  Detail & Related papers  (2025-09-16T09:48:52Z)
• Task-Oriented Low-Label Semantic Communication With Self-Supervised Learning [67.06363342414397]
  Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages.<n>Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation.<n>We propose a self-supervised learning-based semantic communication framework (SLSCom) to enhance task inference performance.
  arXiv  Detail & Related papers  (2025-05-26T13:06:18Z)
• Cluster-Based Multi-Agent Task Scheduling for Space-Air-Ground Integrated Networks [60.085771314013044]
  Low-altitude economy holds significant potential for development in areas such as communication and sensing.<n>We propose a Clustering-based Multi-agent Deep Deterministic Policy Gradient (CMADDPG) algorithm to address the multi-UAV cooperative task scheduling challenges in SAGIN.
  arXiv  Detail & Related papers  (2024-12-14T06:17:33Z)
• Cooperative and Collaborative Multi-Task Semantic Communication for Distributed Sources [8.22548024950756]
  We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs)<n>We propose an SemCom system that supports multi-task processing through cooperation on the transmitter side via split structure and collaboration on the receiver side.
  arXiv  Detail & Related papers  (2024-11-04T15:07:48Z)
• A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode [53.71516191515285]
  The low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system. We propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks.
  arXiv  Detail & Related papers  (2024-08-29T06:57:45Z)
• Semantic Communication for Cooperative Multi-Task Processing over Wireless Networks [8.766411351797885]
  We introduce the concept of a "semantic source", allowing multiple semantic interpretations from a single observation. We formulated an end-to-end optimization problem taking into account the communication channel. Our findings highlight that cooperative multi-tasking is not always beneficial.
  arXiv  Detail & Related papers  (2024-04-12T14:03:41Z)
• Generalising Multi-Agent Cooperation through Task-Agnostic Communication [7.380444448047908]
  Existing communication methods for multi-agent reinforcement learning (MARL) in cooperative multi-robot problems are almost exclusively task-specific, training new communication strategies for each unique task. We address this inefficiency by introducing a communication strategy applicable to any task within a given environment. Our objective is to learn a fixed-size latent Markov state from a variable number of agent observations. Our method enables seamless adaptation to novel tasks without fine-tuning the communication strategy, gracefully supports scaling to more agents than present during training, and detects out-of-distribution events in an environment.
  arXiv  Detail & Related papers  (2024-03-11T14:20:13Z)
• Semi-supervised Multi-task Learning for Semantics and Depth [88.77716991603252]
  Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance. We propose the Semi-supervised Multi-Task Learning (MTL) method to leverage the available supervisory signals from different datasets. We present a domain-aware discriminator structure with various alignment formulations to mitigate the domain discrepancy issue among datasets.
  arXiv  Detail & Related papers  (2021-10-14T07:43:39Z)
• Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation [87.1188556802942]
  We present an approach for encoding visual task relationships to improve model performance in an Unsupervised Domain Adaptation (UDA) setting. We propose a novel Cross-Task Relation Layer (CTRL), which encodes task dependencies between the semantic and depth predictions. Furthermore, we propose an Iterative Self-Learning (ISL) training scheme, which exploits semantic pseudo-labels to provide extra supervision on the target domain.
  arXiv  Detail & Related papers  (2021-05-17T13:42:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.