<p>Data-driven machine learning models have increasingly been applied to survival analysis in recent years. However, these models require sufficient training samples, which is often impractical due to privacy, security, and legal constraints. As a result, survival data is typically distributed across multiple institutions and cannot be directly aggregated. In this study, we propose DA-FedSurX, a federated survival analysis framework that integrates gradient boosted survival trees (GBST) with a data augmentation strategy to effectively analyze distributed and private survival data. Unlike previous deep learning-based federated survival models, DA-FedSurX employs histogram-based GBST, which improves computational efficiency while maintaining strong interpretability. Furthermore, our data augmentation component enhances model robustness under Non-IID data distributions, a common challenge in federated learning. Extensive experiments on both simulated and real-world survival datasets demonstrate that DA-FedSurX significantly outperforms state-of-the-art deep survival models, including DeepSurv, DeepHit, CoxCC, and NnetSurvival, in terms of predictive accuracy. These results confirm the potential of DA-FedSurX as an effective and interpretable solution for federated survival analysis.</p>