A novel framework employing federated learning has been developed to address challenges in cross-vendor medical imaging collaboration while preserving patient data privacy. Published in 2025, this approach enables machine learning models to be trained collaboratively across institutions with different imaging equipment without centralizing data. By overcoming standardization issues in imaging vendor differences, this technology promises enhanced generalizability and performance of AI diagnostic tools in diverse clinical settings, accelerating innovation in medical image analysis.