# Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning

arXiv: https://arxiv.org/abs/2503.14084 · HTML: https://mlsanity.com/paper/2503.14084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 14 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Channel-Adaptive Wireless Image Transmission with OFDM](https://mlsanity.com/paper/2205.02417/) (2022)
- [Achieving Linear Speedup in Non-IID Federated Bilevel Learning](https://mlsanity.com/paper/2302.05412/) (2023)
- [Joint Source-Channel Coding for Channel-Adaptive Digital Semantic Communications](https://mlsanity.com/paper/2311.08146/) (2023)
- [SNR-adaptive deep joint source-channel coding for wireless image transmission](https://mlsanity.com/paper/2102.00202/) (2021)
- [An Efficient Federated Learning Framework for Training Semantic Communication System](https://mlsanity.com/paper/2310.13236/) (2023)
- [Federated Contrastive Learning for Personalized Semantic Communication](https://mlsanity.com/paper/2406.09182/) (2024)

## Abstract (arXiv)

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.