Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions of a readily accessible data source, where in practice, data is mainly created on the client side. Due to privacy and security concerns, the transmission of data is restricted, which is necessary for conventional centralized training schemes. To address this challenge, we explore semantic communication in a federated learning (FL) setting that utilizes client data without leaking privacy. Additionally, we design our system to tackle the communication overhead by reducing the quantity of information delivered in each global round. In this way, we can save significant bandwidth for resource-limited devices and reduce overall network traffic. Finally, we introduce a mechanism to aggregate the global model from clients, called FedLol. Extensive simulation results demonstrate the effectiveness of our proposed technique compared to baseline methods.

翻译：语义通信因其缓解数据冗余的能力，已成为下一代通信系统的支柱。大多数语义通信系统基于先进的深度学习模型构建，其训练性能严重依赖数据可用性。现有研究常假设数据源易于获取，然而实践中数据主要由客户端产生。由于隐私和安全问题，数据传输受到限制，而这正是传统集中式训练方案所必需的。为应对这一挑战，我们探索了联邦学习（FL）场景下的语义通信，利用客户端数据且不泄露隐私。此外，我们设计了系统以降低每轮全局通信中传递的信息量，从而解决通信开销问题。通过这种方式，可为资源受限设备节省大量带宽，并减少整体网络流量。最后，我们引入了一种从客户端聚合全局模型的机制，称为FedLol。大量仿真结果表明，与基准方法相比，我们提出的技术具有有效性。