The widespread success of nnU-Net as a state-of-the-art tool for medical image segmentation has driven its adoption as a baseline, but its limited portability and lack of clinical integration have limited broader deployment in real-world healthcare workflows. To address these challenges, we present the MONet Bundle, extending nnU-Net within the MONAI ecosystem, providing a modular benchmarking tool for Federated Learning (FL) that is directly compatible with downstream clinical operations such as model deployment, active learning, and DICOM-based PACS integration. MONet enables federated training across distributed clinical datasets while maintaining standardized preprocessing and harmonized workflows. Its flexibility is validated on two representative segmentation tasks: lymphoma lesion segmentation in PET-CT and brain tumor segmentation from the BraTS challenge. In both settings, MONet’s federated models consistently outperformed cross-site baselines and approached, or in some cases outperformed, the performance of centralized task-fusion models with minimal user intervention. The code is available at https://github.com/SimoneBendazzoli93/MONet-Bundle .