학술논문
Personalizing Semantic Communication: A Foundation Model Approach
Document Type
Conference
Source
2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) Signal Processing Advances in Wireless Communications (SPAWC), 2024 IEEE 25th International Workshop on. :846-850 Sep, 2024
Subject
Language
ISSN
1948-3252
Abstract
In multi-user task-oriented semantic communication, existing deep learning (DL)-based approaches may fail to achieve high scalability with satisfactory performance, especially in the context of providing semantic services in diverse downstream semantic-aware tasks over heterogeneous networks. Recent advances in foundation models (FMs), which display excellent knowledge representation capabilities, have expanded the boundaries of what is possible with DL-based semantic communication. In this paper, we establish an FM-based multi-user semantic communication framework via personalized federated parameter-efficient finetuning, in which each user equipment is provided with personalized semantic services in diverse downstream tasks. Our proposed approach achieves scalable and generalized performance in a computation-efficient manner. We conduct experiments with state-of-the-art FMs (i.e., LLaMA-7B and CLIP) across diverse system configurations to demonstrate the efficacy of the proposed approach.