NeuroFATE-MS: Privacy-Aware Federated Temporal Learning for Short-Term Multiple Sclerosis Progression Prediction

Predicting short-term progression in multiple sclerosis (MS) from longitudinal clinical data remains challenging because visits occur at irregular intervals, patient trajectories vary substantially, and simulated federated clients often exhibit non-IID data distributions. Privacy constraints further limit centralized data aggregation, reducing the suitability of conventional temporal prediction models. We propose a federated learning framework for MS progression prediction that combines local and global temporal modeling with client-level imbalance handling under simulated privacy-preserving federated constraints. The model uses a bidirectional LSTM to capture short-range temporal dynamics and a Transformer encoder to model longer-range dependencies in irregularly sampled sequences. A cross-attention mechanism fuses these representations. Each client applies conditional trajectory augmentation with ProxiWGANP and a proximity constraint. Training uses federated averaging with gradient clipping and Gaussian noise injection, while privacy loss is estimated through Rényi differential privacy accounting. We evaluated the framework on a single-center longitudinal cohort of 517 patients and 4184 visits. After excluding first visits without historical context and visits without determinable 6-month follow-up labels, we derived 3184 eligible prediction windows. We partitioned these windows into 10 simulated federated clients using Dirichlet sampling and evaluated performance with a strict within-patient temporal split. Across five independent training seeds, the proposed method achieved a mean AUROC of 0.930 with a standard deviation of 0.003 and a mean AUPRC of 0.513 with a standard deviation of 0.005. For the primary seed, patient-level bootstrap estimates were AUROC 0.930 (95% CI: 0.912–0.948), AUPRC 0.515 (95% CI: 0.490–0.540), and ECE 0.014. These results suggest that the proposed framework can support accurate short-term MS progression prediction in a simulated federated setting. However, because the evaluation used a single-center cohort and simulated federation, the findings provide methodological evidence and require external multi-center validation before clinical deployment.