Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
To address the poor generalization and difficulty in achieving personalization in semantic communication—caused by combinatorial explosion of channel state information (CSI) and data in multi-user dynamic channel environments—this paper proposes a CSI-aware dual-pipeline joint source-channel coding (JSCC) architecture. The architecture integrates CSI-driven adaptive semantic encoding/decoding with personalized federated learning and introduces a zero-optimization-gap method for solving non-convex loss functions, enabling concurrent global robustness and local personalization optimization. Experiments across diverse SNR distributions and benchmark datasets demonstrate that the proposed approach reduces average semantic distortion by 23.7% compared to baseline methods, accelerates convergence by 41%, and significantly improves both semantic fidelity and transmission efficiency.