Federated learning (FL) emerged as a popular distributed algorithm to train machine learning models on edge devices while preserving data privacy. However, FL systems face challenges due to client-side computational constraints and from a lack of robustness to naturally occurring common corruptions such as noise, blur, and weather effects. Existing robust training methods are computationally expensive and unsuitable for resource-constrained clients. We propose a novel data-agnostic robust training (DART) plug-in that can be deployed in any FL system to enhance robustness at zero client overhead. DART operates at the server-side and does not require private data access, ensuring seamless integration in existing FL systems. Extensive experiments showcase DART's ability to enhance robustness of state-of-the-art FL systems, establishing it as a practical and scalable solution for real-world robust FL deployment.