Semantic Communication (SC) focuses on transmitting only the semantic information rather than the raw data. This approach offers an efficient solution to the issue of spectrum resource utilization caused by the various intelligent applications on Mobile Users (MUs). Generative Artificial Intelligence (GAI) models have recently exhibited remarkable content generation and signal processing capabilities, presenting new opportunities for enhancing SC. Therefore, we propose a GAI-assisted SC (GSC) model deployed between MUs and the Base Station (BS). Then, to train the GSC model using the local data of MUs while ensuring privacy and accommodating heterogeneous requirements of MUs, we introduce Personalized Semantic Federated Learning (PSFL). This approach incorporates a novel Personalized Local Distillation (PLD) and Adaptive Global Pruning (AGP). In PLD, each MU selects a personalized GSC model as a mentor tailored to its local resources and a unified Convolutional Neural Networks (CNN)-based SC (CSC) model as a student. This mentor model is then distilled into the student model for global aggregation. In AGP, we perform network pruning on the aggregated global model according to real-time communication environments, reducing communication energy. Finally, numerical results demonstrate the feasibility and efficiency of the proposed PSFL scheme.

翻译：语义通信（SC）专注于传输语义信息而非原始数据，为移动用户（MU）上各类智能应用引发的频谱资源利用问题提供了高效解决方案。生成式人工智能（GAI）模型近期展现出卓越的内容生成与信号处理能力，为增强语义通信带来了新机遇。为此，我们提出一种部署于移动用户与基站（BS）之间的GAI辅助语义通信（GSC）模型。随后，为利用移动用户的本地数据训练GSC模型，同时保障隐私性并适应移动用户的异构需求，我们引入个性化语义联邦学习（PSFL）。该方法融合了新颖的个性化本地蒸馏（PLD）与自适应全局剪枝（AGP）。在PLD中，每个移动用户根据其本地资源选择个性化GSC模型作为导师模型，并采用统一的基于卷积神经网络（CNN）的语义通信（CSC）模型作为学生模型，将导师模型的知识蒸馏至学生模型以进行全局聚合。在AGP中，我们根据实时通信环境对聚合后的全局模型进行网络剪枝，以降低通信能耗。最终，数值结果验证了所提PSFL方案的可行性与高效性。