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

arXiv:2607.21426 · cs.LG, cs.IT, eess.SP, stat.ML · Submitted 2026-07-23 · Read on arXiv

Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Bho Matthiesen, Armin Dekorsy

cs.LG, cs.IT, eess.SP, stat.ML

Submitted: 2026-07-23

Comments: This work has been submitted to the IEEE for possible publication

Code: https://github.com/ant-uni-bremen/Semantic

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Clustering — 58× in this paper · explained in 2 episodes
• Cooperation — 13× in this paper · explained in 2 episodes
• Decoder — 6× in this paper · explained in 2 episodes
• Semantic Clustering — 4× in this paper · explained in 2 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Hyperparameters — 2× in this paper · explained in 2 episodes
• Mutual Information — 2× in this paper · explained in 2 episodes
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Communication Efficiency — 1× in this paper · explained in 2 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Conv2dbased Convolutional Neural Network — 1× in this paper
• Convolutional Neural Network (CNN) — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Federated Learning — 1× in this paper · explained in 19 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Machine Learning (ML) — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 3 episodes
• Monte Carlo Sampling — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 3 episodes
• Uniform Manifold Approximation and Projection — 1× in this paper
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Task Grouping for Automated Multi-Task Machine Learning via Task Affinity Prediction
• Semantic-Aware Task Clustering for Federated Cooperative Multi-Task Semantic Communication
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Auto-Encoding Variational Bayes
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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