Semantic communication can significantly improve bandwidth utilization in wireless systems by exploiting the meaning behind raw data. However, the advancements achieved through semantic communication are closely dependent on the development of deep learning (DL) models for joint source-channel coding (JSCC) encoder/decoder techniques, which require a large amount of data for training. To address this data-intensive nature of DL models, federated learning (FL) has been proposed to train a model in a distributed manner, where the server broadcasts the DL model to clients in the network for training with their local data. However, the conventional FL approaches suffer from catastrophic degradation when client data are from different domains. In contrast, in this paper, a novel FL framework is proposed to address this domain shift by constructing the global representation, which aligns with the local features of the clients to preserve the semantics of different data domains. In addition, the dominance problem of client domains with a large number of samples is identified and, then, addressed with a domain-aware aggregation approach. This work is the first to consider the domain shift in training the semantic communication system for the image reconstruction task. Finally, simulation results demonstrate that the proposed approach outperforms the model-contrastive FL (MOON) framework by 0.5 for PSNR values under three domains at an SNR of 1 dB, and this gap continues to widen as the channel quality improves.

翻译：语义通信通过利用原始数据背后的含义，能显著提升无线系统的带宽利用率。然而，语义通信所取得的进展高度依赖于用于联合信源信道编码（JSCC）编码器/解码器技术的深度学习（DL）模型的发展，这些模型需要大量数据进行训练。为应对DL模型的数据密集型特性，联邦学习（FL）被提出以分布式方式训练模型，其中服务器将DL模型广播给网络中的客户端，供其使用本地数据进行训练。然而，当客户端数据来自不同领域时，传统FL方法会遭受灾难性性能下降。相比之下，本文提出了一种新颖的FL框架，通过构建与客户端局部特征对齐的全局表征来解决这种域偏移问题，从而保留不同数据域的语义信息。此外，本文识别了样本数量庞大的客户端领域的主导问题，并采用域感知聚合方法加以解决。本工作是首个在图像重建任务的语义通信系统训练中考虑域偏移的研究。最终，仿真结果表明，在信噪比为1 dB、涉及三个领域的情况下，所提方法在峰值信噪比（PSNR）值上优于模型对比联邦学习（MOON）框架0.5，且随着信道质量改善，这一差距持续扩大。