In this letter, we design a federated contrastive learning (FedCL) framework
aimed at supporting personalized semantic communication. Our FedCL enables
collaborative training of local semantic encoders across multiple clients and a
global semantic decoder owned by the base station. This framework supports
heterogeneous semantic encoders since it does not require client-side model
aggregation. Furthermore, to tackle the semantic imbalance issue arising from
heterogeneous datasets across distributed clients, we employ contrastive
learning to train a semantic centroid generator (SCG). This generator obtains
representative global semantic centroids that exhibit intra-semantic
compactness and inter-semantic separability. Consequently, it provides superior
supervision for learning discriminative local semantic features. Additionally,
we conduct theoretical analysis to quantify the convergence performance of
FedCL. Simulation results verify the superiority of the proposed FedCL
framework compared to other distributed learning benchmarks in terms of task
performance and robustness under different numbers of clients and channel
conditions, especially in low signal-to-noise ratio and highly heterogeneous
data scenarios.

设计了一个支持个性化语义通信的联邦对比学习 (FedCL) 框架，它通过在多个客户端之间协作训练本地语义编码器和基站所拥有的全局语义解码器来实现。该框架不需要客户端模型聚合，支持异构语义编码器。此外，为了解决分布式客户端间异构数据集引起的语义不平衡问题，我们采用对比学习训练语义质心生成器 (SCG)。该生成器获得代表性的全局语义质心，具有内语义紧凑性和跨语义可分离性。因此，它为学习有区分性的本地语义特征提供优越的监督。此外，我们进行了理论分析，量化了 FedCL 的收敛性能。仿真结果验证了所提出的 FedCL 框架在任务性能和稳健性方面相对于其他分布式学习基准的优越性，尤其在低信噪比和高度异构数据场景下。