OptiGradTrust: Byzantine-Robust Federated Learning with Multi-Feature Gradient Analysis and Reinforcement Learning-Based Trust Weighting

--Federated Learning (FL) enables collaborative model training across distributed medical institutions while preserving patient privacy, but remains vulnerable to Byzantine attacks and statistical heterogeneity. T o address convergence challenges under data heterogeneity, we develop FedBN-Prox (FedBN-P), combining Federated Batch Normalization with proximal regularization for optimal accuracy-convergence trade-offs. Extensive evaluation across MNIST, CIF AR-10, and Alzheimer's MRI datasets under various Byzantine attack scenarios demonstrates significant improvements over state-of-the-art defenses, achieving up to +1.6 percentage points over FLGuard under non-IID conditions while maintaining robust performance against diverse attack patterns through our adaptive learning approach. In recent years, Federated Learning (FL) has emerged as a powerful paradigm for training deep neural networks across geographically distributed hospitals while preserving patient privacy under stringent regulations such as HIP AA and GDPR [1]-[4]. Recent advances in federated learning for healthcare have shown significant promise in addressing privacy-sensitive medical data challenges through innovative approaches such as secure multi-party computation [5] and blockchain-enhanced frameworks [6] while enabling secure collaborative learning across medical institutions. As illustrated in Figure 1, this collaborative framework allows medical institutions to exchange model updates rather than raw MRI scans, enabling multi-institutional collaboration--for instance, a small rural hospital with just a handful of Alzheimer's MRI scans can still contribute to, and benefit from, a model jointly trained with top-tier research centers. However, real-world FL deployments must cope with two intertwined challenges. First, Byzantine updates --malicious or low-quality gradient submissions--can severely skew the global model and compromise clinical reliability [7], [8], arising from hospitals with insufficient labeled data, poor-quality imaging equipment, or adversarial behavior.