  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.
