<p>The increasing deployment of distributed machine learning models necessitates robust optimization methods that can tolerate adversarial or faulty nodes. In this work, we propose a robust gradient aggregation method for distributed stochastic gradient descent that leverages hyperbolic geometry. Specifically, local gradients computed at individual nodes are embedded into hyperbolic space using the Poincaré ball model, and their geometric median is computed as a robust aggregate. This aggregated gradient is then mapped back to Euclidean space for the gradient update. We also show that existing robust gradient aggregation methods like Krum can be improved using hyperbolic space. Compared to existing robust aggregation methods, our hyperbolic approach offers improved separation of outlier updates. We provide theoretical convergence guarantees and validate our method on benchmark datasets as well as on a traffic forecasting task, demonstrating its efficacy in mitigating Byzantine failures in distributed federated learning environments.</p>