TACToC: Enabling Task-Adaptive Communication System with a Task-Describable Encoder

Abstract

Task-oriented communications (ToC), also known as semantic communications, offers a promising paradigm for offloading deep neural network (DNN) inference from edge devices to more powerful remote servers. By transmitting only task-specific information, ToC significantly reduces the communication overhead inherent in conventional offloading systems. However, existing ToC approaches exhibit limited task adaptivity, requiring retraining or separate encoders for each task—hindering their practicality in real-world multi-task settings. In this work, we present TACToC, the first ToC system to achieve task adaptivity with a single encoder, enabled by a novel task-describable encoder structure. TACToC first maps input data into a semantically structured feature space using a self-supervised vision transformer (SSL-ViT), providing a foundation for flexible task-specific feature selection. The encoder then dynamically selects and transmits only important features for the target task, without per-task optimization. This is realized through two key mechanisms: (i) relevant feature estimation, which quantifies each feature's contribution to task inference, and (ii) prompt-based task embedding, which injects task-specific information to guide selective transmission for each task. Extensive experiments demonstrate that TACToC reduces communication cost by up to 1.5-14.5× within the range that allows for a 3%p degradation while supporting diverse vision tasks with seamless task integration and transition. This work marks a significant step toward practical and unified ToC systems for dynamic and multi-task AI services.