Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
Journal:
arXiv
Published Date:
Mar 18, 2025
Abstract
Semantic communication is designed to tackle issues like bandwidth
constraints and high latency in communication systems. However, in complex
network topologies with multiple users, the enormous combinations of client
data and channel state information (CSI) pose significant challenges for
existing semantic communication architectures. To improve the generalization
ability of semantic communication models in complex scenarios while meeting the
personalized needs of each user in their local environments, we propose a novel
personalized federated learning framework with dual-pipeline joint
source-channel coding based on channel awareness model (PFL-DPJSCCA). Within
this framework, we present a method that achieves zero optimization gap for
non-convex loss functions. Experiments conducted under varying SNR
distributions validate the outstanding performance of our framework across
diverse datasets.
