FedMGS addresses client- and node-level modality imbalance in federated graph learning by synthesizing latent semantic representations. It integrates an availability-aware graph encoder, prototype-guided semantic synthesizer, and reliability-calibrated fusion mechanism to recover missing modalities while preserving semantic alignment. Experiments show FedMGS achieves up to 17.41% performance gains over baselines across four tasks.