Secure Federated Learning for Brain Tumor Segmentation Under Non-IID and Missing-Modality Conditions

This paper presents a lightweight (<1M-parameter) federated 3D U-Net for multi-region (WT/TC/ET) brain tumor segmentation under realistic hospital conditions — non-IID client data and a per-client missing MRI modality — evaluated with a modality-aware Dice/HD95 protocol. We show that ignoring a client's missing modality at evaluation time inflates pooled Dice by 0.075–0.12 points, a bias not disclosed in prior federated segmentation work. We also validate a full secure-deployment stack (TLS, ECDSA node authentication, SecAgg+) on real gRPC connections, and demonstrate that a gradient-inversion attack recovering a 15.51 dB PSNR patch from a raw update drops to −66.03 dB once SecAgg+ masking is applied — an 81 dB gap quantifying secure aggregation's practical privacy benefit.