Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models
To address the high computational overhead of large models and the slow, channel-agnostic inference of standard knowledge distillation (KD) in task-oriented semantic communication, this paper proposes a channel-aware fast knowledge distillation framework. Our method introduces three key innovations: (1) a pre-stored compression mechanism that eliminates redundant inference; (2) a channel-adaptive module enabling dynamic semantic adjustment based on real-time channel conditions; and (3) an information-bottleneck-driven loss function that jointly optimizes semantic fidelity and channel robustness. Experiments demonstrate that the proposed approach achieves comparable task accuracy while reducing model size by 3.2×, decreasing inference latency by 67%, and cutting training data requirements by 45%. It significantly outperforms existing KD and semantic communication baselines in efficiency, adaptability, and resource efficiency.