用生成式AI提升语义通信效率，支持用户个性化学习与隐私保护。
Personalized Federated Learning for Generative AI-Assisted Semantic Communications
- 各用户自选个性化的生成模型作为教师，指导本地模型训练。
- 全局模型按实时环境动态剪枝，通信能耗降低37%以上。
- 适合资源异构、注重隐私的移动终端语义通信场景。
语义通信（SC）聚焦传输语义信息而非原始数据，可有效缓解智能应用带来的频谱资源压力。生成式人工智能（GAI）具备出色的生成与信号处理能力，为提升语义通信带来新机遇。为此，我们提出部署于移动用户（MUs）与基站（BS）间的生成式语义通信（GSC）模型。为利用用户本地数据训练GSC模型，同时保障隐私并适配用户异构需求，引入个性化语义联邦学习（PSFL）。该方法结合新型个性化本地蒸馏（PLD）与自适应全局剪枝（AGP）。在PLD中，每个MU选择一个基于本地资源定制的个性化GSC模型作为教师，统一的卷积神经网络（CNN）-基语义通信（CSC）模型作为学生，将教师模型知识蒸馏至学生模型以进行全局聚合。在AGP中，根据实时通信环境对聚合后的全局模型进行网络剪枝，降低通信能耗。数值结果验证了所提PSFL方案的可行性与高效性。
原文摘要 · Abstract (English)
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.
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