Personalized Federated Learning for Generative AI-Assisted Semantic Communications

• URL: http://arxiv.org/abs/2410.02450v1
• Date: Thu, 3 Oct 2024 12:52:36 GMT
• Title: Personalized Federated Learning for Generative AI-Assisted Semantic Communications
• Authors: Yubo Peng, Feibo Jiang, Li Dong, Kezhi Wang, Kun Yang,
• Abstract summary: We propose a GAI-assisted Semantic Communication (SC) model deployed between Mobile Users (MUs) and the Base Station (BS) To train the GSC model using the local data of MUs, we introduce Personalized Semantic Federated Learning (PSFL) 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. In AGP, we perform network pruning on the aggregated global model according to real-time communication environments, reducing communication energy.
• Score: 29.931169585178818
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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