Byzantine robust federated learning for heterogeneous brain MRI using multisignal gradient fingerprinting and...

Federated learning enables collaborative training across institutions without centralizing patient data, but remains vulnerable to malicious clients and severe non-IID data heterogeneity. We propose a trust-aware federated learning framework for brain MRI that combines multi-signal gradient fingerprinting with adaptive aggregation to achieve Byzantine robustness. Each client update is characterized by a six-dimensional fingerprint (variational-autoencoder reconstruction error, cosine similarity to a server reference, peer similarity, gradient norm, sign consistency, and Monte Carlo Shapley contribution). A dual-attention module and a reinforcement-learning controller map these signals into trust weights and integrate with FedBN-P (Federated Batch Normalization with Proximal regularization), an optimizer co-designed for stability under heterogeneous and adversarial conditions. We evaluate on MNIST, CIFAR-10, Alzheimer's MRI, and the OASIS brain-MRI cohort (approximately 87 test samples, used strictly as proof-of-concept) under both standard and strengthened threat models (up to 40% malicious clients). Attack-specific ablation confirms a defense-in-depth design: VAE fingerprinting is the primary noise-attack defense (3.60 pp accuracy drop upon removal), Shapley values safeguard accuracy under scaling (10.16 pp drop), and reinforcement learning improves detection consistency under dynamic attack schedules. Three-seed paired-test validation further shows detection F1 outperforms FLTrust by up to 44 pp on Non-IID Gaussian noise; a white-box adaptive attacker degrades the detector but not model accuracy, confirming the layered design. End-to-end wall-clock overhead is + 8.8% over FedAvg with identical communication volume. The framework achieves F1 above 0.98 for gradient-scaling attacks while preserving accuracy under magnitude-preserving attacks where explicit detection remains limited. Multi-site validation on larger federated cohorts (e.g., ADNI, UK Biobank, FeTS) is required before any clinical-deployment claim can be made.