Recent studies on semantic communication commonly rely on neural network (NN) based transceivers such as deep joint source and channel coding (DeepJSCC). Unlike traditional transceivers, these neural transceivers are trainable using actual source data and channels, enabling them to extract and communicate semantics. On the flip side, each neural transceiver is inherently biased towards specific source data and channels, making different transceivers difficult to understand intended semantics, particularly upon their initial encounter. To align semantics over multiple neural transceivers, we propose a distributed learning based solution, which leverages split learning (SL) and partial NN fine-tuning techniques. In this method, referred to as SL with layer freezing (SLF), each encoder downloads a misaligned decoder, and locally fine-tunes a fraction of these encoder-decoder NN layers. By adjusting this fraction, SLF controls computing and communication costs. Simulation results confirm the effectiveness of SLF in aligning semantics under different source data and channel dissimilarities, in terms of classification accuracy, reconstruction errors, and recovery time for comprehending intended semantics from misalignment.

翻译：近期关于语义通信的研究通常依赖于基于神经网络（NN）的收发器，例如深度联合源信道编码（DeepJSCC）。与传统收发器不同，这些神经收发器可利用实际源数据和信道进行训练，从而能够提取和传递语义。然而，每个神经收发器天然倾向于特定的源数据和信道，导致不同收发器难以理解目标语义，尤其是在首次交互时。为了在多个神经收发器之间对齐语义，我们提出了一种基于分布式学习的解决方案，该方案利用分割学习（SL）和部分神经网络微调技术。在这种称为“冻结层分割学习”（SLF）的方法中，每个编码器下载一个未对齐的解码器，并本地微调这些编码器-解码器神经网络层的一部分。通过调整这一比例，SLF可控制计算与通信成本。仿真结果验证了SLF在不同源数据和信道差异下对齐语义的有效性，具体体现在分类准确率、重建误差以及从未对齐状态恢复理解目标语义的时间上。