Magnetic Resonance Imaging (MRI) is highly susceptible to acquisition artifacts, which degrade image quality and compromise medical diagnosis. In clinical settings, the absence of robust recovery methods forces technologists to repeat corrupted scans, unnecessarily increasing operational costs and patient discomfort. Although Deep Learning methods have shown potential for correcting MRI artifacts, their development is hindered by the limited availability of artifact-corrupted images. When available, data remain dispersed across hospitals and cannot be centralized due to privacy and regulatory constraints. Compounding this, scanner heterogeneity requires models to be trained on multi-institutional data to achieve efficient correction. To address these challenges, we propose OHANA, an end-to-end solution for synthetic artifact generation and correction in multi-contrast brain MRI. We first introduce a synthetic data generator that simulates multiple artifact types, creating artifact-corrupted training datasets. Using this augmented data, we extend MC2-Net to perform artifact correction across four MRI contrasts. OHANA also integrates Federated Learning, allowing multiple institutions to collaboratively train models without sharing data. Experiments demonstrate that OHANA outperforms state-of-the-art artifact-correction approaches, achieving a 17.2% improvement in the Structural Similarity Index Measure. A radiologist assessed the realism of the generated artifacts and the diagnostic fidelity of the corrected images. These results highlight the potential of OHANA to improve medical diagnosis.